Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device

ABSTRACT

A three-dimensional data encoding method is a three-dimensional data encoding method of encoding a plurality of three-dimensional points, and includes: selecting one prediction mode for calculating, using attribute information of a second three-dimensional point in a vicinity of a first three-dimensional point, a predicted value of attribute information of the first three-dimensional point; calculating a predicted value according to the one prediction mode; calculating a prediction residual that is a difference between the attribute information of the first three-dimensional point and the predicted value; and generating a bitstream that includes the one prediction mode and the prediction residual, and in the calculating of the predicted value, a predetermined fixed value is calculated as a predicted value calculated according to the one prediction mode in a case in which a predicted value based on the attribute information of the second three-dimensional point is not assigned to the one prediction mode.

BACKGROUND 1. Technical Field

The present disclosure relates to a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, and a three-dimensional data decoding device.

2. Description of the Related Art

Devices or services utilizing three-dimensional data are expected tofind their widespread use in a wide range of fields, such as computervision that enables autonomous operations of cars or robots, mapinformation, monitoring, infrastructure inspection, and videodistribution. Three-dimensional data is obtained through various meansincluding a distance sensor such as a rangefinder, as well as a stereocamera and a combination of a plurality of monocular cameras.

Methods of representing three-dimensional data include a method known asa point cloud scheme that represents the shape of a three-dimensionalstructure by a point group (point cloud) in a three-dimensional space.In the point cloud scheme, the positions and colors of a point cloud arestored. While point cloud is expected to be a mainstream method ofrepresenting three-dimensional data, a massive amount of data of a pointcloud necessitates compression of the amount of three-dimensional databy encoding for accumulation and transmission, as in the case of atwo-dimensional moving picture (examples include MPEG-4 AVC and HEVCstandardized by MPEG).

Meanwhile, point cloud compression is partially supported by, forexample, an open-source library (Point Cloud Library) for pointcloud-related processing.

Furthermore, a technique for searching for and displaying a facilitylocated in the surroundings of the vehicle is known (for example, seeInternational Publication WO 2014/020663).

SUMMARY

There have been demands for correctly decoding attribute information indecoding three-dimensional data.

The present disclosure aims to provide a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, or a three-dimensional data decoding device whichis capable of correctly decoding attribute information in decodingthree-dimensional data.

A three-dimensional data encoding method according to an aspect of thepresent disclosure is a three-dimensional data encoding method ofencoding a plurality of three-dimensional points. The three-dimensionaldata encoding method includes: selecting one prediction mode among twoor more prediction modes for calculating, using attribute information ofone or more second three-dimensional points in a vicinity of a firstthree-dimensional point, a predicted value of attribute information ofthe first three-dimensional point; calculating a predicted valueaccording to the one prediction mode selected; calculating a predictionresidual that is a difference between the attribute information of thefirst three-dimensional point and the predicted value calculated; andgenerating a bitstream that includes the one prediction mode selectedand the prediction residual. In the calculating of the predicted value,a predetermined fixed value is assigned as a predicted value calculatedaccording to the one prediction mode selected in a case in which apredicted value based on the attribute information of the one or moresecond three-dimensional points is not assigned to the one predictionmode selected.

A three-dimensional data decoding method according to an aspect of thepresent disclosure is a three-dimensional data decoding method ofdecoding a plurality of three-dimensional points. The three-dimensionaldata decoding method includes: obtaining a prediction mode and aprediction residual of a first three-dimensional point among theplurality of three-dimensional points by obtaining a bitstream;calculating a predicted value according to the prediction mode obtained;and calculating attribute information of the first three-dimensionalpoint by adding the predicted value and the prediction residual. In thecalculating of the predicted value, a predetermined fixed value isassigned as a predicted value calculated according to the predictionmode obtained in a case in which a predicted value based on attributeinformation of one or more second three-dimensional points in a vicinityof the first three-dimensional point is not assigned to the predictionmode obtained.

The present disclosure can provide a three-dimensional data encodingmethod, a three-dimensional data decoding method, a three-dimensionaldata encoding device, or a three-dimensional data decoding device whichcan correctly decode attribute information in decoding three-dimensionaldata.

BRIEF DESCRIPTION OF DRAWINGS

These and other objects, advantages and features of the disclosure willbecome apparent from the following description thereof taken inconjunction with the accompanying drawings that illustrate a specificembodiment of the present disclosure.

FIG. 1 is a diagram showing the structure of encoded three-dimensionaldata according to Embodiment 1;

FIG. 2 is a diagram showing an example of prediction structures amongSPCs that belong to the lowermost layer in a GOS according to Embodiment1;

FIG. 3 is a diagram showing an example of prediction structures amonglayers according to Embodiment 1;

FIG. 4 is a diagram showing an example order of encoding GOSs accordingto Embodiment 1;

FIG. 5 is a diagram showing an example order of encoding GOSs accordingto Embodiment 1;

FIG. 6 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 1;

FIG. 7 is a flowchart of encoding processes according to Embodiment 1;

FIG. 8 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 1;

FIG. 9 is a flowchart of decoding processes according to Embodiment 1;

FIG. 10 is a diagram showing an example of meta information according toEmbodiment 1;

FIG. 11 is a diagram showing an example structure of a SWLD according toEmbodiment 2;

FIG. 12 is a diagram showing example operations performed by a serverand a client according to Embodiment 2;

FIG. 13 is a diagram showing example operations performed by the serverand a client according to Embodiment 2;

FIG. 14 is a diagram showing example operations performed by the serverand the clients according to Embodiment 2;

FIG. 15 is a diagram showing example operations performed by the serverand the clients according to Embodiment 2;

FIG. 16 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 2;

FIG. 17 is a flowchart of encoding processes according to Embodiment 2;

FIG. 18 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 2;

FIG. 19 is a flowchart of decoding processes according to Embodiment 2;

FIG. 20 is a diagram showing an example structure of a WLD according toEmbodiment 2;

FIG. 21 is a diagram showing an example octree structure of the WLDaccording to Embodiment 2;

FIG. 22 is a diagram showing an example structure of a SWLD according toEmbodiment 2;

FIG. 23 is a diagram showing an example octree structure of the SWLDaccording to Embodiment 2;

FIG. 24 is a block diagram of a three-dimensional data creation deviceaccording to Embodiment 3;

FIG. 25 is a block diagram of a three-dimensional data transmissiondevice according to Embodiment 3;

FIG. 26 is a block diagram of a three-dimensional information processingdevice according to Embodiment 4;

FIG. 27 is a block diagram of a three-dimensional data creation deviceaccording to Embodiment 5;

FIG. 28 is a diagram showing a structure of a system according toEmbodiment 6;

FIG. 29 is a block diagram of a client device according to Embodiment 6;

FIG. 30 is a block diagram of a server according to Embodiment 6;

FIG. 31 is a flowchart of a three-dimensional data creation processperformed by the client device according to Embodiment 6;

FIG. 32 is a flowchart of a sensor information transmission processperformed by the client device according to Embodiment 6;

FIG. 33 is a flowchart of a three-dimensional data creation processperformed by the server according to Embodiment 6;

FIG. 34 is a flowchart of a three-dimensional map transmission processperformed by the server according to Embodiment 6;

FIG. 35 is a diagram showing a structure of a variation of the systemaccording to Embodiment 6;

FIG. 36 is a diagram showing a structure of the server and clientdevices according to Embodiment 6;

FIG. 37 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 7;

FIG. 38 is a diagram showing an example of a prediction residualaccording to Embodiment 7;

FIG. 39 is a diagram showing an example of a volume according toEmbodiment 7;

FIG. 40 is a diagram showing an example of an octree representation ofthe volume according to Embodiment 7;

FIG. 41 is a diagram showing an example of bit sequences of the volumeaccording to Embodiment 7;

FIG. 42 is a diagram showing an example of an octree representation of avolume according to Embodiment 7;

FIG. 43 is a diagram showing an example of the volume according toEmbodiment 7;

FIG. 44 is a diagram for describing an intra prediction processaccording to Embodiment 7;

FIG. 45 is a diagram for describing a rotation and translation processaccording to Embodiment 7;

FIG. 46 is a diagram showing an example syntax of an RT flag and RTinformation according to Embodiment 7;

FIG. 47 is a diagram for describing an inter prediction processaccording to Embodiment 7;

FIG. 48 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 7;

FIG. 49 is a flowchart of a three-dimensional data encoding processperformed by the three-dimensional data encoding device according toEmbodiment 7;

FIG. 50 is a flowchart of a three-dimensional data decoding processperformed by the three-dimensional data decoding device according toEmbodiment 7;

FIG. 51 is a diagram illustrating an example of three-dimensional pointsaccording to Embodiment 8;

FIG. 52 is a diagram illustrating an example of setting LoDs accordingto Embodiment 8;

FIG. 53 is a diagram illustrating an example of setting LoDs accordingto Embodiment 8;

FIG. 54 is a diagram illustrating an example of attribute information tobe used for predicted values according to Embodiment 8;

FIG. 55 is a diagram illustrating examples of exponential-Golomb codesaccording to Embodiment 8;

FIG. 56 is a diagram indicating a process on exponential-Golomb codesaccording to Embodiment 8;

FIG. 57 is a diagram indicating an example of a syntax in attributeheader according to Embodiment 8;

FIG. 58 is a diagram indicating an example of a syntax in attribute dataaccording to Embodiment 8;

FIG. 59 is a flowchart of a three-dimensional data encoding processaccording to Embodiment 8;

FIG. 60 is a flowchart of an attribute information encoding processaccording to Embodiment 8;

FIG. 61 is a diagram indicating processing on exponential-Golomb codesaccording to Embodiment 8;

FIG. 62 is a diagram indicating an example of a reverse lookup tableindicating relationships between remaining codes and the values thereofaccording to Embodiment 8;

FIG. 63 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 8;

FIG. 64 is a flowchart of an attribute information decoding processaccording to Embodiment 8;

FIG. 65 is a block diagram of a three-dimensional data encoding deviceaccording to Embodiment 8;

FIG. 66 is a block diagram of a three-dimensional data decoding deviceaccording to Embodiment 8;

FIG. 67 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 8;

FIG. 68 is a flowchart of a three-dimensional data decoding processaccording to Embodiment 8;

FIG. 69 is a diagram showing a first example of a table representingpredicted values calculated in prediction modes according to Embodiment9;

FIG. 70 is a diagram showing examples of attribute information items(pieces of attribute information) used as the predicted values accordingto Embodiment 9;

FIG. 71 is a diagram showing a second example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 72 is a diagram showing a third example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 73 is a diagram showing a fourth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 74 is a diagram showing a fifth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 75 is a diagram showing a sixth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 76 is a diagram showing a seventh example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 77 is a diagram showing an eighth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 78 is a diagram showing a first example of a binarization table inbinarizing and encoding prediction mode values according to Embodiment9;

FIG. 79 is a diagram showing a second example of a binarization table inbinarizing and encoding the prediction mode values according toEmbodiment 9;

FIG. 80 is a diagram showing a third example of a binarization table inbinarizing and encoding the prediction mode values according toEmbodiment 9;

FIG. 81 is a diagram for describing an example of encoding binary datain the binarization table in binarizing and encoding the predictionmodes according to Embodiment 9;

FIG. 82 is a flowchart of an example of encoding a prediction mode valueaccording to Embodiment 9;

FIG. 83 is a flowchart of an example of decoding a prediction mode valueaccording to Embodiment 9;

FIG. 84 is a diagram showing another example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9;

FIG. 85 is a diagram for describing an example of encoding binary datain the binarization table in binarizing and encoding the predictionmodes according to Embodiment 9;

FIG. 86 is flowchart of another example of encoding a prediction modevalue according to Embodiment 9;

FIG. 87 is a flowchart of another example of decoding a prediction modevalue according to Embodiment 9;

FIG. 88 is a flowchart of an example of a process of determining whetheror not a prediction mode value is fixed under condition A in encodingaccording to Embodiment 9;

FIG. 89 is a flowchart of an example of a process of determining whetherthe prediction mode value is fixed or decoded under condition A indecoding according to Embodiment 9;

FIG. 90 is a diagram for describing maximum absolute differential valuemaxdiff according to Embodiment 9;

FIG. 91 is a diagram showing an example of a syntax according toEmbodiment 9;

FIG. 92 is a diagram showing an example of a syntax according toEmbodiment 9;

FIG. 93 is a flowchart of a three-dimensional data encoding process by athree-dimensional data encoding device according to Embodiment 9;

FIG. 94 is a flowchart of an attribute information encoding processaccording to Embodiment 9;

FIG. 95 is a flowchart of a predicted value calculation process by thethree-dimensional data encoding device according to Embodiment 9;

FIG. 96 is a flowchart of a prediction mode selection process accordingto Embodiment 9;

FIG. 97 is a flowchart of a prediction mode selection process thatminimizes cost according to Embodiment 9;

FIG. 98 is a flowchart of a three-dimensional data decoding process by athree-dimensional data decoding device according to Embodiment 9;

FIG. 99 is a flowchart of an attribute information decoding processaccording to Embodiment 9;

FIG. 100 is a flowchart of a predicted value calculation process by thethree-dimensional data decoding device according to Embodiment 9;

FIG. 101 is a flowchart of a prediction mode decoding process accordingto Embodiment 9;

FIG. 102 is a block diagram showing a configuration of an attributeinformation encoder included in the three-dimensional data encodingdevice according to Embodiment 9;

FIG. 103 is a block diagram showing a configuration of an attributeinformation decoder included in the three-dimensional data decodingdevice according to Embodiment 9;

FIG. 104 is a block diagram showing a configuration of thethree-dimensional data encoding device according to Embodiment 9;

FIG. 105 is a block diagram showing a configuration of thethree-dimensional data decoding device according to Embodiment 9;

FIG. 106 is a flowchart of processes of the three-dimensional dataencoding device according to Embodiment 9;

FIG. 107 is a flowchart of processes of the three-dimensional datadecoding device according to Embodiment 9;

FIG. 108 is a diagram according to Embodiment 10 which illustrates anexample of a table indicating predicted values calculated according torespective prediction modes by a three-dimensional data encoding device;

FIG. 109 is a diagram according to Embodiment 10 which illustrates anexample of a binarization table indicating a case in which predictionmode values are encoded by binarization;

FIG. 110 is a diagram according to Embodiment 10 which illustrates anexample of a table indicating predicted values calculated according tothe respective prediction modes by a three-dimensional data decodingdevice;

FIG. 111 is a diagram according to Embodiment 10 which illustrates anexample of a table indicating predicted values calculated according tothe respective prediction modes by the three-dimensional data encodingdevice;

FIG. 112 is a diagram according to Embodiment 10 which illustrates anexample of a binarization table indicating a case in which theprediction mode values are encoded by binarization;

FIG. 113 is a diagram according to Embodiment 10 which illustrates anexample of a table indicating predicted values calculated according tothe respective prediction modes by the three-dimensional data decodingdevice;

FIG. 114 illustrates an example according to Embodiment 10 whichillustrates an initial table used for processing of assigning predictedvalues to the prediction modes;

FIG. 115 is a diagram according to Embodiment 10 which illustrates anexample of a table which is generated using the initial table and inwhich predicted values are calculated according to the respectiveprediction modes by the three-dimensional data encoding device;

FIG. 116 is a flowchart according to Embodiment 10 which illustrates anexample of processing of calculating a predicted value;

FIG. 117 is a flowchart according to Embodiment 10 which illustratesprocessing of selecting a prediction mode;

FIG. 118 is a flowchart according to Embodiment 10 which illustratesprocessing of selecting a prediction mode with minimum cost;

FIG. 119 is a flowchart according to Embodiment 10 which illustratesthree-dimensional data decoding processing performed by thethree-dimensional data decoding device;

FIG. 120 is a flowchart according to Embodiment 10 which illustratesattribute information decoding processing;

FIG. 121 is a flowchart according to Embodiment 10 which illustratesprocessing of calculating a predicted value;

FIG. 122 is a flowchart according to Embodiment 10 which illustratesanother example of processing of calculating a predicted value;

FIG. 123 is a flowchart according to Embodiment 10 which illustratesprocessing of selecting a prediction mode;

FIG. 124 is a flowchart according to Embodiment 10 which illustratesprocessing of calculating a predicted value;

FIG. 125 is a block diagram according to Embodiment 10 which illustratesa configuration of the three-dimensional data encoding device;

FIG. 126 is a block diagram according to Embodiment 10 which illustratesa configuration of the three-dimensional data decoding device;

FIG. 127 is a flowchart according to Embodiment 10 which illustratesprocessing performed by the three-dimensional data encoding device; and

FIG. 128 is a flowchart according to Embodiment 10 which illustratesprocessing performed by the three-dimensional data decoding device.

DETAILED DESCRIPTION OF THE EMBODIMENTS

A three-dimensional data encoding method according to an aspect of thepresent disclosure is a three-dimensional data encoding method ofencoding a plurality of three-dimensional points. The three-dimensionaldata encoding method includes: selecting one prediction mode among twoor more prediction modes for calculating, using attribute information ofone or more second three-dimensional points in a vicinity of a firstthree-dimensional point, a predicted value of attribute information ofthe first three-dimensional point; calculating a predicted valueaccording to the one prediction mode selected; calculating a predictionresidual that is a difference between the attribute information of thefirst three-dimensional point and the predicted value calculated; andgenerating a bitstream that includes the one prediction mode selectedand the prediction residual. In the calculating of the predicted value,a predetermined fixed value is assigned as a predicted value calculatedaccording to the one prediction mode selected in a case in which apredicted value based on the attribute information of the one or moresecond three-dimensional points is not assigned to the one predictionmode selected.

With this, a predetermined fixed value is assigned as a predicted valuecalculated according to a prediction mode to which one or more predictedvalues are not assigned. For this reason, a bitstream to be generatedincludes a prediction mode and a prediction residual that is calculatedaccording to the predicted value calculated according to the selectedprediction mode. Accordingly, the three-dimensional data decoding devicecan calculate a predicted value that coincides with a predicted valuethat is calculated in the encoding since, in the same manner as thethree-dimensional data encoding method, a predetermined fixed value isassigned as a predicted value calculated according to a prediction modeto which one or more predicted values are not assigned when decodingattribute information of a three-dimensional point to be processed fromthe obtained bitstream. Therefore, the three-dimensional data decodingdevice can correctly decode the attribute information of thethree-dimensional point to be processed.

Furthermore, in the calculating of the predicted value, the case inwhich a predicted value based on the attribute information of the one ormore second three-dimensional points is not assigned to the oneprediction mode selected may be a case in which the predicted value isnot assigned to the one prediction mode selected due to a total numberof the one or more second three-dimensional points being less than orequal to a predetermined number.

In addition, the predetermined fixed value may be an initial value.

The predetermined fixed value may also be zero.

Moreover, in the calculating of the predicted value, an average of theattribute information of the one or more second three-dimensional pointsmay be calculated as the predicted value calculated according to a firstprediction mode among the two or more prediction modes, and theattribute information of the one or more second three-dimensional pointsmay be calculated as the predicted value calculated according to asecond prediction mode among the two or more prediction modes.

Furthermore, the two or more prediction modes are indicated byprediction mode values each having a different value, and a predictionmode value indicating a prediction mode to which the average is assignedas a predicted value may be less than a prediction mode value indicatinga prediction mode to which the attribute information of the one or moresecond three-dimensional points is assigned as a predicted value.

In addition, the two or more prediction modes are indicated byprediction mode values each having a different value, and a predictionmode value indicating a prediction mode to which attribute informationof one second three-dimensional point among the one or more secondthree-dimensional points is assigned as a predicted value may be lessthan a prediction mode value indicating a prediction mode to whichattribute information of another second three-dimensional point amongthe one or more second three-dimensional points is assigned as apredicted value. The another second three-dimensional point is locatedfurther away from the first three-dimensional point than the one secondthree-dimensional point.

A three-dimensional data decoding method according to an aspect of thepresent disclosure is a three-dimensional data decoding method ofdecoding a plurality of three-dimensional points. The three-dimensionaldata decoding method includes: obtaining a prediction mode and aprediction residual of a first three-dimensional point among theplurality of three-dimensional points by obtaining a bitstream;calculating a predicted value according to the prediction mode obtained;and calculating attribute information of the first three-dimensionalpoint by adding the predicted value and the prediction residual. In thecalculating of the predicted value, a predetermined fixed value isassigned as a predicted value calculated according to the predictionmode obtained in a case in which a predicted value based on attributeinformation of one or more second three-dimensional points in a vicinityof the first three-dimensional point is not assigned to the predictionmode obtained.

Accordingly, a predicted value that coincides with a predicted valuethat is calculated in encoding can be calculated since, in the samemanner as the three-dimensional data encoding method, a predeterminedfixed value is assigned as a predicted value calculated according to aprediction mode to which one or more predicted values are not assignedwhen decoding attribute information of a three-dimensional point to beprocessed from the obtained bitstream. Therefore, the attributeinformation of the three-dimensional point to be processed can becorrectly decoded.

In addition, in the calculating of the predicted value, the case inwhich a predicted value based on the attribute information of the one ormore second three-dimensional points is not assigned to the predictionmode obtained may be a case in which a predicted value is not assignedto the prediction mode obtained due to a total number of the one or moresecond three-dimensional points being less than or equal to apredetermined number.

Moreover, the predetermined fixed value may be an initial value.

The predetermined fixed value may also be zero.

Furthermore, in the calculating of the predicted value, an average ofthe attribute information of the one or more second three-dimensionalpoints may be calculated as the predicted value calculated according toa first prediction mode among two or more prediction modes, and theattribute information of the one or more second three-dimensional pointsmay be calculated as the predicted value calculated according to asecond prediction mode among the two or more prediction modes.

In addition, the two or more prediction modes are indicated byprediction mode values each having a different value, and a predictionmode value indicating a prediction mode to which the average is assignedas a predicted value may be less than a prediction mode value indicatinga prediction mode to which the attribute information of the one or moresecond three-dimensional points is assigned as a predicted value.

Moreover, two or more prediction modes are indicated by prediction modevalues each having a different value, and a prediction mode valueindicating a prediction mode to which attribute information of onesecond three-dimensional point among the one or more secondthree-dimensional points is assigned as a predicted value may be lessthan a prediction mode value indicating a prediction mode to whichattribute information of another second three-dimensional point amongthe one or more second three-dimensional points is assigned as apredicted value. The another second three-dimensional point is locatedfurther away from the first three-dimensional point than the one secondthree-dimensional point.

Furthermore, a three-dimensional data encoding device according to anaspect of the present disclosure is a three-dimensional data encodingdevice that encodes a plurality of three-dimensional points. Thethree-dimensional data encoding device includes a processor and memory.Using the memory, the processor: selects one prediction mode among twoor more prediction modes for calculating, using attribute information ofone or more second three-dimensional points in a vicinity of a firstthree-dimensional point, a predicted value of attribute information ofthe first three-dimensional point; calculates a predicted valueaccording to the one prediction mode selected; calculates a predictionresidual that is a difference between the attribute information of thefirst three-dimensional point and the predicted value calculated;generates a bitstream that includes the one prediction mode selected andthe prediction residual; and in the calculating of the predicted value,assigns a predetermined fixed value as a predicted value calculatedaccording to the one prediction mode selected in a case in which apredicted value based on the attribute information of the one or moresecond three-dimensional points is not assigned to the one predictionmode selected.

With this, a predetermined fixed value is assigned as a predicted valuecalculated according to a prediction mode to which one or more predictedvalues are not assigned. For this reason, a bitstream to be generatedincludes a prediction mode and a prediction residual that is calculatedaccording to the predicted value calculated according to the selectedprediction mode. Accordingly, the three-dimensional data decoding devicecan calculate a predicted value that coincides with a predicted valuethat is calculated in the encoding since, in the same manner as thethree-dimensional data encoding method, a predetermined fixed value isassigned as a predicted value calculated according to a prediction modeto which one or more predicted values are not assigned when decodingattribute information of a three-dimensional point to be processed fromthe obtained bitstream. Therefore, the three-dimensional data decodingdevice can correctly decode the attribute information of thethree-dimensional point to be processed.

In addition, a three-dimensional data decoding device according to anaspect of the present disclosure is a three-dimensional data decodingdevice that decodes a plurality of three-dimensional points. Thethree-dimensional data decoding device includes a processor and memory.Using the memory, the processor: obtains a prediction mode and aprediction residual of a first three-dimensional point among theplurality of three-dimensional points by obtaining a bitstream;calculates a predicted value according to the prediction mode obtained;calculates attribute information of the first three-dimensional point byadding the predicted value and the prediction residual; and in thecalculating of the predicted value, assigns a predetermined fixed valueas a predicted value calculated according to the prediction modeobtained in a case in which a predicted value based on attributeinformation of one or more second three-dimensional points in a vicinityof the first three-dimensional point is not assigned to the predictionmode obtained.

Accordingly, a predicted value that coincides with a predicted valuethat is calculated in encoding can be calculated since, in the samemanner as the three-dimensional data encoding method, a predeterminedfixed value is assigned as a predicted value calculated according to aprediction mode to which one or more predicted values are not assignedwhen decoding attribute information of a three-dimensional point to beprocessed from the obtained bitstream. Therefore, the attributeinformation of the three-dimensional point to be processed can becorrectly decoded.

It is to be noted that these general or specific aspects may beimplemented as a system, a method, an integrated circuit, a computerprogram, or a computer-readable recording medium such as a CD-ROM, ormay be implemented as any optional combination of a system, a method, anintegrated circuit, a computer program, and a recording medium.

Hereinafter, exemplary embodiments will be described in detail withreference to the drawings. Note that the embodiments described beloweach show a specific example of the present disclosure. The numericalvalues, shapes, materials, elements, the arrangement and connection ofthe elements, steps, order of the steps, etc. indicated in the followingembodiments are mere examples, and therefore are not intended to limitthe scope of the present disclosure. Therefore, among elements in thefollowing embodiments, those not recited in any one of the broadest,independent claims are described as optional elements.

Embodiment 1

First, the data structure of encoded three-dimensional data (hereinafteralso referred to as encoded data) according to the present embodimentwill be described. FIG. 1 is a diagram showing the structure of encodedthree-dimensional data according to the present embodiment.

In the present embodiment, a three-dimensional space is divided intospaces (SPCs), which correspond to pictures in moving picture encoding,and the three-dimensional data is encoded on a SPC-by-SPC basis. EachSPC is further divided into volumes (VLMs), which correspond tomacroblocks, etc. in moving picture encoding, and predictions andtransforms are performed on a VLM-by-VLM basis. Each volume includes aplurality of voxels (VXLs), each being a minimum unit in which positioncoordinates are associated. Note that prediction is a process ofgenerating predictive three-dimensional data analogous to a currentprocessing unit by referring to another processing unit, and encoding adifferential between the predictive three-dimensional data and thecurrent processing unit, as in the case of predictions performed ontwo-dimensional images. Such prediction includes not only spatialprediction in which another prediction unit corresponding to the sametime is referred to, but also temporal prediction in which a predictionunit corresponding to a different time is referred to.

When encoding a three-dimensional space represented by point cloud datasuch as a point cloud, for example, the three-dimensional data encodingdevice (hereinafter also referred to as the encoding device) encodes thepoints in the point cloud or points included in the respective voxels ina collective manner, in accordance with a voxel size. Finer voxelsenable a highly-precise representation of the three-dimensional shape ofa point cloud, while larger voxels enable a rough representation of thethree-dimensional shape of a point cloud.

Note that the following describes the case where three-dimensional datais a point cloud, but three-dimensional data is not limited to a pointcloud, and thus three-dimensional data of any format may be employed.

Also note that voxels with a hierarchical structure may be used. In sucha case, when the hierarchy includes n levels, whether a sampling pointis included in the n−1th level or lower levels (levels below the n-thlevel) may be sequentially indicated. For example, when only the n-thlevel is decoded, and the n−1th level or lower levels include a samplingpoint, the n-th level can be decoded on the assumption that a samplingpoint is included at the center of a voxel in the n-th level.

Also, the encoding device obtains point cloud data, using, for example,a distance sensor, a stereo camera, a monocular camera, a gyroscopesensor, or an inertial sensor.

As in the case of moving picture encoding, each SPC is classified intoone of at least the three prediction structures that include: intra SPC(I-SPC), which is individually decodable; predictive SPC (P-SPC) capableof only a unidirectional reference; and bidirectional SPC (B-SPC)capable of bidirectional references. Each SPC includes two types of timeinformation: decoding time and display time.

Furthermore, as shown in FIG. 1 , a processing unit that includes aplurality of SPCs is a group of spaces (GOS), which is a random accessunit. Also, a processing unit that includes a plurality of GOSs is aworld (WLD).

The spatial region occupied by each world is associated with an absoluteposition on earth, by use of, for example, GPS, or latitude andlongitude information. Such position information is stored asmeta-information. Note that meta-information may be included in encodeddata, or may be transmitted separately from the encoded data.

Also, inside a GOS, all SPCs may be three-dimensionally adjacent to oneanother, or there may be a SPC that is not three-dimensionally adjacentto another SPC.

Note that the following also describes processes such as encoding,decoding, and reference to be performed on three-dimensional dataincluded in processing units such as GOS, SPC, and VLM, simply asperforming encoding/to encode, decoding/to decode, referring to, etc. ona processing unit. Also note that three-dimensional data included in aprocessing unit includes, for example, at least one pair of a spatialposition such as three-dimensional coordinates and an attribute valuesuch as color information.

Next, the prediction structures among SPCs in a GOS will be described. Aplurality of SPCs in the same GOS or a plurality of VLMs in the same SPCoccupy mutually different spaces, while having the same time information(the decoding time and the display time).

A SPC in a GOS that comes first in the decoding order is an I-SPC. GOSscome in two types: closed GOS and open GOS. A closed GOS is a GOS inwhich all SPCs in the GOS are decodable when decoding starts from thefirst I-SPC. Meanwhile, an open GOS is a GOS in which a different GOS isreferred to in one or more SPCs preceding the first I-SPC in the GOS inthe display time, and thus cannot be singly decoded.

Note that in the case of encoded data of map information, for example, aWLD is sometimes decoded in the backward direction, which is opposite tothe encoding order, and thus backward reproduction is difficult whenGOSs are interdependent. In such a case, a closed GOS is basically used.

Each GOS has a layer structure in height direction, and SPCs aresequentially encoded or decoded from SPCs in the bottom layer.

FIG. 2 is a diagram showing an example of prediction structures amongSPCs that belong to the lowermost layer in a GOS. FIG. 3 is a diagramshowing an example of prediction structures among layers.

A GOS includes at least one I-SPC. Of the objects in a three-dimensionalspace, such as a person, an animal, a car, a bicycle, a signal, and abuilding serving as a landmark, a small-sized object is especiallyeffective when encoded as an I-SPC. When decoding a GOS at a lowthroughput or at a high speed, for example, the three-dimensional datadecoding device (hereinafter also referred to as the decoding device)decodes only I-SPC(s) in the GOS.

The encoding device may also change the encoding interval or theappearance frequency of I-SPCs, depending on the degree of sparsenessand denseness of the objects in a WLD.

In the structure shown in FIG. 3 , the encoding device or the decodingdevice encodes or decodes a plurality of layers sequentially from thebottom layer (layer 1). This increases the priority of data on theground and its vicinity, which involve a larger amount of information,when, for example, a self-driving car is concerned.

Regarding encoded data used for a drone, for example, encoding ordecoding may be performed sequentially from SPCs in the top layer in aGOS in height direction.

The encoding device or the decoding device may also encode or decode aplurality of layers in a manner that the decoding device can have arough grasp of a GOS first, and then the resolution is graduallyincreased. The encoding device or the decoding device may performencoding or decoding in the order of layers 3, 8, 1, 9 . . . , forexample.

Next, the handling of static objects and dynamic objects will bedescribed.

A three-dimensional space includes scenes or still objects such as abuilding and a road (hereinafter collectively referred to as staticobjects), and objects with motion such as a car and a person(hereinafter collectively referred to as dynamic objects). Objectdetection is separately performed by, for example, extracting keypointsfrom point cloud data, or from video of a camera such as a stereocamera. In this description, an example method of encoding a dynamicobject will be described.

A first method is a method in which a static object and a dynamic objectare encoded without distinction. A second method is a method in which adistinction is made between a static object and a dynamic object on thebasis of identification information.

For example, a GOS is used as an identification unit. In such a case, adistinction is made between a GOS that includes SPCs constituting astatic object and a GOS that includes SPCs constituting a dynamicobject, on the basis of identification information stored in the encodeddata or stored separately from the encoded data.

Alternatively, a SPC may be used as an identification unit. In such acase, a distinction is made between a SPC that includes VLMsconstituting a static object and a SPC that includes VLMs constituting adynamic object, on the basis of the identification information thusdescribed.

Alternatively, a VLM or a VXL may be used as an identification unit. Insuch a case, a distinction is made between a VLM or a VXL that includesa static object and a VLM or a VXL that includes a dynamic object, onthe basis of the identification information thus described.

The encoding device may also encode a dynamic object as at least one VLMor SPC, and may encode a VLM or a SPC including a static object and aSPC including a dynamic object as mutually different GOSs. When the GOSsize is variable depending on the size of a dynamic object, the encodingdevice separately stores the GOS size as meta-information.

The encoding device may also encode a static object and a dynamic objectseparately from each other, and may superimpose the dynamic object ontoa world constituted by static objects. In such a case, the dynamicobject is constituted by at least one SPC, and each SPC is associatedwith at least one SPC constituting the static object onto which the eachSPC is to be superimposed. Note that a dynamic object may be representednot by SPC(s) but by at least one VLM or VXL.

The encoding device may also encode a static object and a dynamic objectas mutually different streams.

The encoding device may also generate a GOS that includes at least oneSPC constituting a dynamic object. The encoding device may further setthe size of a GOS including a dynamic object (GOS_M) and the size of aGOS including a static object corresponding to the spatial region ofGOS_M at the same size (such that the same spatial region is occupied).This enables superimposition to be performed on a GOS-by-GOS basis.

SPC(s) included in another encoded GOS may be referred to in a P-SPC ora B-SPC constituting a dynamic object. In the case where the position ofa dynamic object temporally changes, and the same dynamic object isencoded as an object in a GOS corresponding to a different time,referring to SPC(s) across GOSs is effective in terms of compressionrate.

The first method and the second method may be selected in accordancewith the intended use of encoded data. When encoded three-dimensionaldata is used as a map, for example, a dynamic object is desired to beseparated, and thus the encoding device uses the second method.Meanwhile, the encoding device uses the first method when the separationof a dynamic object is not required such as in the case wherethree-dimensional data of an event such as a concert and a sports eventis encoded.

The decoding time and the display time of a GOS or a SPC are storable inencoded data or as meta-information. All static objects may have thesame time information. In such a case, the decoding device may determinethe actual decoding time and display time. Alternatively, a differentvalue may be assigned to each GOS or SPC as the decoding time, and thesame value may be assigned as the display time. Furthermore, as in thecase of the decoder model in moving picture encoding such asHypothetical Reference Decoder (HRD) compliant with HEVC, a model may beemployed that ensures that a decoder can perform decoding without failby having a buffer of a predetermined size and by reading a bitstream ata predetermined bit rate in accordance with the decoding times.

Next, the topology of GOSs in a world will be described. The coordinatesof the three-dimensional space in a world are represented by the threecoordinate axes (x axis, y axis, and z axis) that are orthogonal to oneanother. A predetermined rule set for the encoding order of GOSs enablesencoding to be performed such that spatially adjacent GOSs arecontiguous in the encoded data. In an example shown in FIG. 4 , forexample, GOSs in the x and z planes are successively encoded. After thecompletion of encoding all GOSs in certain x and z planes, the value ofthe y axis is updated. Stated differently, the world expands in the yaxis direction as the encoding progresses. The GOS index numbers are setin accordance with the encoding order.

Here, the three-dimensional spaces in the respective worlds arepreviously associated one-to-one with absolute geographical coordinatessuch as GPS coordinates or latitude/longitude coordinates.Alternatively, each three-dimensional space may be represented as aposition relative to a previously set reference position. The directionsof the x axis, the y axis, and the z axis in the three-dimensional spaceare represented by directional vectors that are determined on the basisof the latitudes and the longitudes, etc. Such directional vectors arestored together with the encoded data as meta-information.

GOSs have a fixed size, and the encoding device stores such size asmeta-information. The GOS size may be changed depending on, for example,whether it is an urban area or not, or whether it is inside or outsideof a room. Stated differently, the GOS size may be changed in accordancewith the amount or the attributes of objects with information values.Alternatively, in the same world, the encoding device may adaptivelychange the GOS size or the interval between I-SPCs in GOSs in accordancewith the object density, etc. For example, the encoding device sets theGOS size to smaller and the interval between I-SPCs in GOSs to shorter,as the object density is higher.

In an example shown in FIG. 5 , to enable random access with a finergranularity, a GOS with a high object density is partitioned into theregions of the third to tenth GOSs. Note that the seventh to tenth GOSsare located behind the third to sixth GOSs.

Next, the structure and the operation flow of the three-dimensional dataencoding device according to the present embodiment will be described.FIG. 6 is a block diagram of three-dimensional data encoding device 100according to the present embodiment. FIG. 7 is a flowchart of an exampleoperation performed by three-dimensional data encoding device 100.

Three-dimensional data encoding device 100 shown in FIG. 6 encodesthree-dimensional data 111, thereby generating encoded three-dimensionaldata 112. Such three-dimensional data encoding device 100 includesobtainer 101, encoding region determiner 102, divider 103, and encoder104.

As shown in FIG. 7 , first, obtainer 101 obtains three-dimensional data111, which is point cloud data (S101).

Next, encoding region determiner 102 determines a current region forencoding from among spatial regions corresponding to the obtained pointcloud data (S102). For example, in accordance with the position of auser or a vehicle, encoding region determiner 102 determines, as thecurrent region, a spatial region around such position.

Next, divider 103 divides the point cloud data included in the currentregion into processing units. The processing units here means units suchas GOSs and SPCs described above. The current region here correspondsto, for example, a world described above. More specifically, divider 103divides the point cloud data into processing units on the basis of apredetermined GOS size, or the presence/absence/size of a dynamic object(S103). Divider 103 further determines the starting position of the SPCthat comes first in the encoding order in each GOS.

Next, encoder 104 sequentially encodes a plurality of SPCs in each GOS,thereby generating encoded three-dimensional data 112 (S104).

Note that although an example is described here in which the currentregion is divided into GOSs and SPCs, after which each GOS is encoded,the processing steps are not limited to this order. For example, stepsmay be employed in which the structure of a single GOS is determined,which is followed by the encoding of such GOS, and then the structure ofthe subsequent GOS is determined.

As thus described, three-dimensional data encoding device 100 encodesthree-dimensional data 111, thereby generating encoded three-dimensionaldata 112. More specifically, three-dimensional data encoding device 100divides three-dimensional data into first processing units (GOSs), eachbeing a random access unit and being associated with three-dimensionalcoordinates, divides each of the first processing units (GOSs) intosecond processing units (SPCs), and divides each of the secondprocessing units (SPCs) into third processing units (VLMs). Each of thethird processing units (VLMs) includes at least one voxel (VXL), whichis the minimum unit in which position information is associated.

Next, three-dimensional data encoding device 100 encodes each of thefirst processing units (GOSs), thereby generating encodedthree-dimensional data 112. More specifically, three-dimensional dataencoding device 100 encodes each of the second processing units (SPCs)in each of the first processing units (GOSs). Three-dimensional dataencoding device 100 further encodes each of the third processing units(VLMs) in each of the second processing units (SPCs).

When a current first processing unit (GOS) is a closed GOS, for example,three-dimensional data encoding device 100 encodes a current secondprocessing unit (SPC) included in such current first processing unit(GOS) by referring to another second processing unit (SPC) included inthe current first processing unit (GOS). Stated differently,three-dimensional data encoding device 100 refers to no secondprocessing unit (SPC) included in a first processing unit (GOS) that isdifferent from the current first processing unit (GOS).

Meanwhile, when a current first processing unit (GOS) is an open GOS,three-dimensional data encoding device 100 encodes a current secondprocessing unit (SPC) included in such current first processing unit(GOS) by referring to another second processing unit (SPC) included inthe current first processing unit (GOS) or a second processing unit(SPC) included in a first processing unit (GOS) that is different fromthe current first processing unit (GOS).

Also, three-dimensional data encoding device 100 selects, as the type ofa current second processing unit (SPC), one of the following: a firsttype (I-SPC) in which another second processing unit (SPC) is notreferred to; a second type (P-SPC) in which another single secondprocessing unit (SPC) is referred to; and a third type in which othertwo second processing units (SPC) are referred to. Three-dimensionaldata encoding device 100 encodes the current second processing unit(SPC) in accordance with the selected type.

Next, the structure and the operation flow of the three-dimensional datadecoding device according to the present embodiment will be described.FIG. 8 is a block diagram of three-dimensional data decoding device 200according to the present embodiment. FIG. 9 is a flowchart of an exampleoperation performed by three-dimensional data decoding device 200.

Three-dimensional data decoding device 200 shown in FIG. 8 decodesencoded three-dimensional data 211, thereby generating decodedthree-dimensional data 212. Encoded three-dimensional data 211 here is,for example, encoded three-dimensional data 112 generated bythree-dimensional data encoding device 100. Such three-dimensional datadecoding device 200 includes obtainer 201, decoding start GOS determiner202, decoding SPC determiner 203, and decoder 204.

First, obtainer 201 obtains encoded three-dimensional data 211 (S201).Next, decoding start GOS determiner 202 determines a current GOS fordecoding (S202). More specifically, decoding start GOS determiner 202refers to meta-information stored in encoded three-dimensional data 211or stored separately from the encoded three-dimensional data todetermine, as the current GOS, a GOS that includes a SPC correspondingto the spatial position, the object, or the time from which decoding isto start.

Next, decoding SPC determiner 203 determines the type(s) (I, P, and/orB) of SPCs to be decoded in the GOS (S203). For example, decoding SPCdeterminer 203 determines whether to (1) decode only I-SPC(s), (2) todecode I-SPC(s) and P-SPCs, or (3) to decode SPCs of all types. Notethat the present step may not be performed, when the type(s) of SPCs tobe decoded are previously determined such as when all SPCs arepreviously determined to be decoded.

Next, decoder 204 obtains an address location within encodedthree-dimensional data 211 from which a SPC that comes first in the GOSin the decoding order (the same as the encoding order) starts. Decoder204 obtains the encoded data of the first SPC from the address location,and sequentially decodes the SPCs from such first SPC (S204). Note thatthe address location is stored in the meta-information, etc.

Three-dimensional data decoding device 200 decodes decodedthree-dimensional data 212 as thus described. More specifically,three-dimensional data decoding device 200 decodes each encodedthree-dimensional data 211 of the first processing units (GOSs), eachbeing a random access unit and being associated with three-dimensionalcoordinates, thereby generating decoded three-dimensional data 212 ofthe first processing units (GOSs). Even more specifically,three-dimensional data decoding device 200 decodes each of the secondprocessing units (SPCs) in each of the first processing units (GOSs).Three-dimensional data decoding device 200 further decodes each of thethird processing units (VLMs) in each of the second processing units(SPCs).

The following describes meta-information for random access. Suchmeta-information is generated by three-dimensional data encoding device100, and included in encoded three-dimensional data 112 (211).

In the conventional random access for a two-dimensional moving picture,decoding starts from the first frame in a random access unit that isclose to a specified time. Meanwhile, in addition to times, randomaccess to spaces (coordinates, objects, etc.) is assumed to be performedin a world.

To enable random access to at least three elements of coordinates,objects, and times, tables are prepared that associate the respectiveelements with the GOS index numbers. Furthermore, the GOS index numbersare associated with the addresses of the respective first I-SPCs in theGOSs. FIG. 10 is a diagram showing example tables included in themeta-information. Note that not all the tables shown in FIG. 10 arerequired to be used, and thus at least one of the tables is used.

The following describes an example in which random access is performedfrom coordinates as a starting point. To access the coordinates (x2, y2,and z2), the coordinates-GOS table is first referred to, which indicatesthat the point corresponding to the coordinates (x2, y2, and z2) isincluded in the second GOS. Next, the GOS-address table is referred to,which indicates that the address of the first I-SPC in the second GOS isaddr(2). As such, decoder 204 obtains data from this address to startdecoding.

Note that the addresses may either be logical addresses or physicaladdresses of an HDD or a memory. Alternatively, information thatidentifies file segments may be used instead of addresses. File segmentsare, for example, units obtained by segmenting at least one GOS, etc.

When an object spans across a plurality of GOSs, the object-GOS tablemay show a plurality of GOSs to which such object belongs. When suchplurality of GOSs are closed GOSs, the encoding device and the decodingdevice can perform encoding or decoding in parallel. Meanwhile, whensuch plurality of GOSs are open GOSs, a higher compression efficiency isachieved by the plurality of GOSs referring to each other.

Example objects include a person, an animal, a car, a bicycle, a signal,and a building serving as a landmark. For example, three-dimensionaldata encoding device 100 extracts keypoints specific to an object from athree-dimensional point cloud, etc., when encoding a world, and detectsthe object on the basis of such keypoints to set the detected object asa random access point.

As thus described, three-dimensional data encoding device 100 generatesfirst information indicating a plurality of first processing units(GOSs) and the three-dimensional coordinates associated with therespective first processing units (GOSs). Encoded three-dimensional data112 (211) includes such first information. The first information furtherindicates at least one of objects, times, and data storage locationsthat are associated with the respective first processing units (GOSs).

Three-dimensional data decoding device 200 obtains the first informationfrom encoded three-dimensional data 211. Using such first information,three-dimensional data decoding device 200 identifies encodedthree-dimensional data 211 of the first processing unit that correspondsto the specified three-dimensional coordinates, object, or time, anddecodes encoded three-dimensional data 211.

The following describes an example of other meta-information. Inaddition to the meta-information for random access, three-dimensionaldata encoding device 100 may also generate and store meta-information asdescribed below, and three-dimensional data decoding device 200 may usesuch meta-information at the time of decoding.

When three-dimensional data is used as map information, for example, aprofile is defined in accordance with the intended use, and informationindicating such profile may be included in meta-information. Forexample, a profile is defined for an urban or a suburban area, or for aflying object, and the maximum or minimum size, etc. of a world, a SPCor a VLM, etc. is defined in each profile. For example, more detailedinformation is required for an urban area than for a suburban area, andthus the minimum VLM size is set to small.

The meta-information may include tag values indicating object types.Each of such tag values is associated with VLMs, SPCs, or GOSs thatconstitute an object. For example, a tag value may be set for eachobject type in a manner, for example, that the tag value “0” indicates“person,” the tag value “1” indicates “car,” and the tag value “2”indicates “signal”. Alternatively, when an object type is hard to judge,or such judgment is not required, a tag value may be used that indicatesthe size or the attribute indicating, for example, whether an object isa dynamic object or a static object.

The meta-information may also include information indicating a range ofthe spatial region occupied by a world.

The meta-information may also store the SPC or VXL size as headerinformation common to the whole stream of the encoded data or to aplurality of SPCs, such as SPCs in a GOS.

The meta-information may also include identification information on adistance sensor or a camera that has been used to generate a pointcloud, or information indicating the positional accuracy of a pointcloud in the point cloud.

The meta-information may also include information indicating whether aworld is made only of static objects or includes a dynamic object.

The following describes variations of the present embodiment.

The encoding device or the decoding device may encode or decode two ormore mutually different SPCs or GOSs in parallel. GOSs to be encoded ordecoded in parallel can be determined on the basis of meta-information,etc. indicating the spatial positions of the GOSs.

When three-dimensional data is used as a spatial map for use by a car ora flying object, etc. in traveling, or for creation of such a spatialmap, for example, the encoding device or the decoding device may encodeor decode GOSs or SPCs included in a space that is identified on thebasis of GPS information, the route information, the zoom magnification,etc.

The decoding device may also start decoding sequentially from a spacethat is close to the self-location or the traveling route. The encodingdevice or the decoding device may give a lower priority to a spacedistant from the self-location or the traveling route than the priorityof a nearby space to encode or decode such distant place. To “give alower priority” means here, for example, to lower the priority in theprocessing sequence, to decrease the resolution (to apply decimation inthe processing), or to lower the image quality (to increase the encodingefficiency by, for example, setting the quantization step to larger).

When decoding encoded data that is hierarchically encoded in a space,the decoding device may decode only the bottom layer in the hierarchy.

The decoding device may also start decoding preferentially from thebottom layer of the hierarchy in accordance with the zoom magnificationor the intended use of the map.

For self-location estimation or object recognition, etc. involved in theself-driving of a car or a robot, the encoding device or the decodingdevice may encode or decode regions at a lower resolution, except for aregion that is lower than or at a specified height from the ground (theregion to be recognized).

The encoding device may also encode point clouds representing thespatial shapes of a room interior and a room exterior separately. Forexample, the separation of a GOS representing a room interior (interiorGOS) and a GOS representing a room exterior (exterior GOS) enables thedecoding device to select a GOS to be decoded in accordance with aviewpoint location, when using the encoded data.

The encoding device may also encode an interior GOS and an exterior GOShaving close coordinates so that such GOSs come adjacent to each otherin an encoded stream. For example, the encoding device associates theidentifiers of such GOSs with each other, and stores informationindicating the associated identifiers into the meta-information that isstored in the encoded stream or stored separately. This enables thedecoding device to refer to the information in the meta-information toidentify an interior GOS and an exterior GOS having close coordinates.

The encoding device may also change the GOS size or the SPC sizedepending on whether a GOS is an interior GOS or an exterior GOS. Forexample, the encoding device sets the size of an interior GOS to smallerthan the size of an exterior GOS. The encoding device may also changethe accuracy of extracting keypoints from a point cloud, or the accuracyof detecting objects, for example, depending on whether a GOS is aninterior GOS or an exterior GOS.

The encoding device may also add, to encoded data, information by whichthe decoding device displays objects with a distinction between adynamic object and a static object. This enables the decoding device todisplay a dynamic object together with, for example, a red box orletters for explanation. Note that the decoding device may display onlya red box or letters for explanation, instead of a dynamic object. Thedecoding device may also display more particular object types. Forexample, a red box may be used for a car, and a yellow box may be usedfor a person.

The encoding device or the decoding device may also determine whether toencode or decode a dynamic object and a static object as a different SPCor GOS, in accordance with, for example, the appearance frequency ofdynamic objects or a ratio between static objects and dynamic objects.For example, when the appearance frequency or the ratio of dynamicobjects exceeds a threshold, a SPC or a GOS including a mixture of adynamic object and a static object is accepted, while when theappearance frequency or the ratio of dynamic objects is below athreshold, a SPC or GOS including a mixture of a dynamic object and astatic object is unaccepted.

When detecting a dynamic object not from a point cloud but fromtwo-dimensional image information of a camera, the encoding device mayseparately obtain information for identifying a detection result (box orletters) and the object position, and encode these items of informationas part of the encoded three-dimensional data. In such a case, thedecoding device superimposes auxiliary information (box or letters)indicating the dynamic object onto a resultant of decoding a staticobject to display it.

The encoding device may also change the sparseness and denseness of VXLsor VLMs in a SPC in accordance with the degree of complexity of theshape of a static object. For example, the encoding device sets VXLs orVLMs at a higher density as the shape of a static object is morecomplex. The encoding device may further determine a quantization step,etc. for quantizing spatial positions or color information in accordancewith the sparseness and denseness of VXLs or VLMs. For example, theencoding device sets the quantization step to smaller as the density ofVXLs or VLMs is higher.

As described above, the encoding device or the decoding device accordingto the present embodiment encodes or decodes a space on a SPC-by-SPCbasis that includes coordinate information.

Furthermore, the encoding device and the decoding device performencoding or decoding on a volume-by-volume basis in a SPC. Each volumeincludes a voxel, which is the minimum unit in which positioninformation is associated.

Also, using a table that associates the respective elements of spatialinformation including coordinates, objects, and times with GOSs or usinga table that associates these elements with each other, the encodingdevice and the decoding device associate any ones of the elements witheach other to perform encoding or decoding. The decoding device uses thevalues of the selected elements to determine the coordinates, andidentifies a volume, a voxel, or a SPC from such coordinates to decode aSPC including such volume or voxel, or the identified SPC.

Furthermore, the encoding device determines a volume, a voxel, or a SPCthat is selectable in accordance with the elements, through extractionof keypoints and object recognition, and encodes the determined volume,voxel, or SPC, as a volume, a voxel, or a SPC to which random access ispossible.

SPCs are classified into three types: I-SPC that is singly encodable ordecodable; P-SPC that is encoded or decoded by referring to any one ofthe processed SPCs; and B-SPC that is encoded or decoded by referring toany two of the processed SPCs.

At least one volume corresponds to a static object or a dynamic object.A SPC including a static object and a SPC including a dynamic object areencoded or decoded as mutually different GOSs. Stated differently, a SPCincluding a static object and a SPC including a dynamic object areassigned to different GOSs.

Dynamic objects are encoded or decoded on an object-by-object basis, andare associated with at least one SPC including a static object. Stateddifferently, a plurality of dynamic objects are individually encoded,and the obtained encoded data of the dynamic objects is associated witha SPC including a static object.

The encoding device and the decoding device give an increased priorityto I-SPC(s) in a GOS to perform encoding or decoding. For example, theencoding device performs encoding in a manner that prevents thedegradation of I-SPCs (in a manner that enables the originalthree-dimensional data to be reproduced with a higher fidelity afterdecoded). The decoding device decodes, for example, only I-SPCs.

The encoding device may change the frequency of using I-SPCs dependingon the sparseness and denseness or the number (amount) of the objects ina world to perform encoding. Stated differently, the encoding devicechanges the frequency of selecting I-SPCs depending on the number or thesparseness and denseness of the objects included in thethree-dimensional data. For example, the encoding device uses I-SPCs ata higher frequency as the density of the objects in a world is higher.

The encoding device also sets random access points on a GOS-by-GOSbasis, and stores information indicating the spatial regionscorresponding to the GOSs into the header information.

The encoding device uses, for example, a default value as the spatialsize of a GOS. Note that the encoding device may change the GOS sizedepending on the number (amount) or the sparseness and denseness ofobjects or dynamic objects. For example, the encoding device sets thespatial size of a GOS to smaller as the density of objects or dynamicobjects is higher or the number of objects or dynamic objects isgreater.

Also, each SPC or volume includes a keypoint group that is derived byuse of information obtained by a sensor such as a depth sensor, agyroscope sensor, or a camera sensor. The coordinates of the keypointsare set at the central positions of the respective voxels. Furthermore,finer voxels enable highly accurate position information.

The keypoint group is derived by use of a plurality of pictures. Aplurality of pictures include at least two types of time information:the actual time information and the same time information common to aplurality of pictures that are associated with SPCs (for example, theencoding time used for rate control, etc.).

Also, encoding or decoding is performed on a GOS-by-GOS basis thatincludes at least one SPC.

The encoding device and the decoding device predict P-SPCs or B-SPCs ina current GOS by referring to SPCs in a processed GOS.

Alternatively, the encoding device and the decoding device predictP-SPCs or B-SPCs in a current GOS, using the processed SPCs in thecurrent GOS, without referring to a different GOS.

Furthermore, the encoding device and the decoding device transmit orreceive an encoded stream on a world-by-world basis that includes atleast one GOS.

Also, a GOS has a layer structure in one direction at least in a world,and the encoding device and the decoding device start encoding ordecoding from the bottom layer. For example, a random accessible GOSbelongs to the lowermost layer. A GOS that belongs to the same layer ora lower layer is referred to in a GOS that belongs to an upper layer.Stated differently, a GOS is spatially divided in a predetermineddirection in advance to have a plurality of layers, each including atleast one SPC. The encoding device and the decoding device encode ordecode each SPC by referring to a SPC included in the same layer as theeach SPC or a SPC included in a layer lower than that of the each SPC.

Also, the encoding device and the decoding device successively encode ordecode GOSs on a world-by-world basis that includes such GOSs. In sodoing, the encoding device and the decoding device write or read outinformation indicating the order (direction) of encoding or decoding asmetadata. Stated differently, the encoded data includes informationindicating the order of encoding a plurality of GOSs.

The encoding device and the decoding device also encode or decodemutually different two or more SPCs or GOSs in parallel.

Furthermore, the encoding device and the decoding device encode ordecode the spatial information (coordinates, size, etc.) on a SPC or aGOS.

The encoding device and the decoding device encode or decode SPCs orGOSs included in an identified space that is identified on the basis ofexternal information on the self-location or/and region size, such asGPS information, route information, or magnification.

The encoding device or the decoding device gives a lower priority to aspace distant from the self-location than the priority of a nearby spaceto perform encoding or decoding.

The encoding device sets a direction at one of the directions in aworld, in accordance with the magnification or the intended use, toencode a GOS having a layer structure in such direction. Also, thedecoding device decodes a GOS having a layer structure in one of thedirections in a world that has been set in accordance with themagnification or the intended use, preferentially from the bottom layer.

The encoding device changes the accuracy of extracting keypoints, theaccuracy of recognizing objects, or the size of spatial regions, etc.included in a SPC, depending on whether an object is an interior objector an exterior object. Note that the encoding device and the decodingdevice encode or decode an interior GOS and an exterior GOS having closecoordinates in a manner that these GOSs come adjacent to each other in aworld, and associate their identifiers with each other for encoding anddecoding.

Embodiment 2

When using encoded data of a point cloud in an actual device or service,it is desirable that necessary information be transmitted/received inaccordance with the intended use to reduce the network bandwidth.However, there has been no such functionality in the structure ofencoding three-dimensional data, nor an encoding method therefor.

The present embodiment describes a three-dimensional data encodingmethod and a three-dimensional data encoding device for providing thefunctionality of transmitting/receiving only necessary information inencoded data of a three-dimensional point cloud in accordance with theintended use, as well as a three-dimensional data decoding method and athree-dimensional data decoding device for decoding such encoded data.

A voxel (VXL) with a feature greater than or equal to a given amount isdefined as a feature voxel (FVXL), and a world (WLD) constituted byFVXLs is defined as a sparse world (SWLD). FIG. 11 is a diagram showingexample structures of a sparse world and a world. A SWLD includes:FGOSs, each being a GOS constituted by FVXLs; FSPCs, each being a SPCconstituted by FVXLs; and FVLMs, each being a VLM constituted by FVXLs.The data structure and prediction structure of a FGOS, a FSPC, and aFVLM may be the same as those of a GOS, a SPC, and a VLM.

A feature represents the three-dimensional position information on a VXLor the visible-light information on the position of a VXL. A largenumber of features are detected especially at a corner, an edge, etc. ofa three-dimensional object. More specifically, such a feature is athree-dimensional feature or a visible-light feature as described below,but may be any feature that represents the position, luminance, or colorinformation, etc. on a VXL.

Used as three-dimensional features are signature of histograms oforientations (SHOT) features, point feature histograms (PFH) features,or point pair feature (PPF) features.

SHOT features are obtained by dividing the periphery of a VXL, andcalculating an inner product of the reference point and the normalvector of each divided region to represent the calculation result as ahistogram. SHOT features are characterized by a large number ofdimensions and high-level feature representation.

PFH features are obtained by selecting a large number of two point pairsin the vicinity of a VXL, and calculating the normal vector, etc. fromeach two point pair to represent the calculation result as a histogram.PFH features are histogram features, and thus are characterized byrobustness against a certain extent of disturbance and also high-levelfeature representation.

PPF features are obtained by using a normal vector, etc. for each twopoints of VXLs. PPF features, for which all VXLs are used, hasrobustness against occlusion.

Used as visible-light features are scale-invariant feature transform(SIFT), speeded up robust features (SURF), or histogram of orientedgradients (HOG), etc. that use information on an image such as luminancegradient information.

A SWLD is generated by calculating the above-described features of therespective VXLs in a WLD to extract FVXLs. Here, the SWLD may be updatedevery time the WLD is updated, or may be regularly updated after theelapse of a certain period of time, regardless of the timing at whichthe WLD is updated.

A SWLD may be generated for each type of features. For example,different SWLDs may be generated for the respective types of features,such as SWLD1 based on SHOT features and SWLD2 based on SIFT features sothat SWLDs are selectively used in accordance with the intended use.Also, the calculated feature of each FVXL may be held in each FVXL asfeature information.

Next, the usage of a sparse world (SWLD) will be described. A SWLDincludes only feature voxels (FVXLs), and thus its data size is smallerin general than that of a WLD that includes all VXLs.

In an application that utilizes features for a certain purpose, the useof information on a SWLD instead of a WLD reduces the time required toread data from a hard disk, as well as the bandwidth and the timerequired for data transfer over a network. For example, a WLD and a SWLDare held in a server as map information so that map information to besent is selected between the WLD and the SWLD in accordance with arequest from a client. This reduces the network bandwidth and the timerequired for data transfer. More specific examples will be describedbelow.

FIG. 12 and FIG. 13 are diagrams showing usage examples of a SWLD and aWLD. As FIG. 12 shows, when client 1, which is a vehicle-mounted device,requires map information to use it for self-location determination,client 1 sends to a server a request for obtaining map data forself-location estimation (S301). The server sends to client 1 the SWLDin response to the obtainment request (S302). Client 1 uses the receivedSWLD to determine the self-location (S303). In so doing, client 1obtains VXL information on the periphery of client 1 through variousmeans including a distance sensor such as a rangefinder, as well as astereo camera and a combination of a plurality of monocular cameras.Client 1 then estimates the self-location information from the obtainedVXL information and the SWLD. Here, the self-location informationincludes three-dimensional position information, orientation, etc. ofclient 1.

As FIG. 13 shows, when client 2, which is a vehicle-mounted device,requires map information to use it for rendering a map such as athree-dimensional map, client 2 sends to the server a request forobtaining map data for map rendering (S311). The server sends to client2 the WLD in response to the obtainment request (S312). Client 2 usesthe received WLD to render a map (S313). In so doing, client 2 uses, forexample, image client 2 has captured by a visible-light camera, etc. andthe WLD obtained from the server to create a rendering image, andrenders such created image onto a screen of a car navigation system,etc.

As described above, the server sends to a client a SWLD when thefeatures of the respective VXLs are mainly required such as in the caseof self-location estimation, and sends to a client a WLD when detailedVXL information is required such as in the case of map rendering. Thisallows for an efficient sending/receiving of map data.

Note that a client may self-judge which one of a SWLD and a WLD isnecessary, and request the server to send a SWLD or a WLD. Also, theserver may judge which one of a SWLD and a WLD to send in accordancewith the status of the client or a network.

Next, a method will be described of switching the sending/receivingbetween a sparse world (SWLD) and a world (WLD).

Whether to receive a WLD or a SWLD may be switched in accordance withthe network bandwidth. FIG. 14 is a diagram showing an example operationin such case. For example, when a low-speed network is used that limitsthe usable network bandwidth, such as in a Long-Term Evolution (LTE)environment, a client accesses the server over a low-speed network(S321), and obtains the SWLD from the server as map information (S322).Meanwhile, when a high-speed network is used that has an adequatelybroad network bandwidth, such as in a WiFi environment, a clientaccesses the server over a high-speed network (S323), and obtains theWLD from the server (S324). This enables the client to obtainappropriate map information in accordance with the network bandwidthsuch client is using.

More specifically, a client receives the SWLD over an LTE network whenin outdoors, and obtains the WLD over a WiFi network when in indoorssuch as in a facility. This enables the client to obtain more detailedmap information on indoor environment.

As described above, a client may request for a WLD or a SWLD inaccordance with the bandwidth of a network such client is using.Alternatively, the client may send to the server information indicatingthe bandwidth of a network such client is using, and the server may sendto the client data (the WLD or the SWLD) suitable for such client inaccordance with the information. Alternatively, the server may identifythe network bandwidth the client is using, and send to the client data(the WLD or the SWLD) suitable for such client.

Also, whether to receive a WLD or a SWLD may be switched in accordancewith the speed of traveling. FIG. 15 is a diagram showing an exampleoperation in such case. For example, when traveling at a high speed(S331), a client receives the SWLD from the server (S332). Meanwhile,when traveling at a low speed (S333), the client receives the WLD fromthe server (S334). This enables the client to obtain map informationsuitable to the speed, while reducing the network bandwidth. Morespecifically, when traveling on an expressway, the client receives theSWLD with a small data amount, which enables the update of rough mapinformation at an appropriate speed. Meanwhile, when traveling on ageneral road, the client receives the WLD, which enables the obtainmentof more detailed map information.

As described above, the client may request the server for a WLD or aSWLD in accordance with the traveling speed of such client.Alternatively, the client may send to the server information indicatingthe traveling speed of such client, and the server may send to theclient data (the WLD or the SWLD) suitable to such client in accordancewith the information. Alternatively, the server may identify thetraveling speed of the client to send data (the WLD or the SWLD)suitable to such client.

Also, the client may obtain, from the server, a SWLD first, from whichthe client may obtain a WLD of an important region. For example, whenobtaining map information, the client first obtains a SWLD for rough mapinformation, from which the client narrows to a region in which featuressuch as buildings, signals, or persons appear at high frequency so thatthe client can later obtain a WLD of such narrowed region. This enablesthe client to obtain detailed information on a necessary region, whilereducing the amount of data received from the server.

The server may also create from a WLD different SWLDs for the respectiveobjects, and the client may receive SWLDs in accordance with theintended use. This reduces the network bandwidth. For example, theserver recognizes persons or cars in a WLD in advance, and creates aSWLD of persons and a SWLD of cars. The client, when wishing to obtaininformation on persons around the client, receives the SWLD of persons,and when wising to obtain information on cars, receives the SWLD ofcars. Such types of SWLDs may be distinguished by information (flag, ortype, etc.) added to the header, etc.

Next, the structure and the operation flow of the three-dimensional dataencoding device (e.g., a server) according to the present embodimentwill be described. FIG. 16 is a block diagram of three-dimensional dataencoding device 400 according to the present embodiment. FIG. 17 is aflowchart of three-dimensional data encoding processes performed bythree-dimensional data encoding device 400.

Three-dimensional data encoding device 400 shown in FIG. 16 encodesinput three-dimensional data 411, thereby generating encodedthree-dimensional data 413 and encoded three-dimensional data 414, eachbeing an encoded stream. Here, encoded three-dimensional data 413 isencoded three-dimensional data corresponding to a WLD, and encodedthree-dimensional data 414 is encoded three-dimensional datacorresponding to a SWLD. Such three-dimensional data encoding device 400includes, obtainer 401, encoding region determiner 402, SWLD extractor403, WLD encoder 404, and SWLD encoder 405.

First, as FIG. 17 shows, obtainer 401 obtains input three-dimensionaldata 411, which is point cloud data in a three-dimensional space (S401).

Next, encoding region determiner 402 determines a current spatial regionfor encoding on the basis of a spatial region in which the point clouddata is present (S402).

Next, SWLD extractor 403 defines the current spatial region as a WLD,and calculates the feature from each VXL included in the WLD. Then, SWLDextractor 403 extracts VXLs having an amount of features greater than orequal to a predetermined threshold, defines the extracted VXLs as FVXLs,and adds such FVXLs to a SWLD, thereby generating extractedthree-dimensional data 412 (S403). Stated differently, extractedthree-dimensional data 412 having an amount of features greater than orequal to the threshold is extracted from input three-dimensional data411.

Next, WLD encoder 404 encodes input three-dimensional data 411corresponding to the WLD, thereby generating encoded three-dimensionaldata 413 corresponding to the WLD (S404). In so doing, WLD encoder 404adds to the header of encoded three-dimensional data 413 informationthat distinguishes that such encoded three-dimensional data 413 is astream including a WLD.

SWLD encoder 405 encodes extracted three-dimensional data 412corresponding to the SWLD, thereby generating encoded three-dimensionaldata 414 corresponding to the SWLD (S405). In so doing, SWLD encoder 405adds to the header of encoded three-dimensional data 414 informationthat distinguishes that such encoded three-dimensional data 414 is astream including a SWLD.

Note that the process of generating encoded three-dimensional data 413and the process of generating encoded three-dimensional data 414 may beperformed in the reverse order. Also note that a part or all of theseprocesses may be performed in parallel.

A parameter “world_type” is defined, for example, as information addedto each header of encoded three-dimensional data 413 and encodedthree-dimensional data 414. world_type=0 indicates that a streamincludes a WLD, and world_type=1 indicates that a stream includes aSWLD. An increased number of values may be further assigned to define alarger number of types, e.g., world_type=2. Also, one of encodedthree-dimensional data 413 and encoded three-dimensional data 414 mayinclude a specified flag. For example, encoded three-dimensional data414 may be assigned with a flag indicating that such stream includes aSWLD. In such a case, the decoding device can distinguish whether suchstream is a stream including a WLD or a stream including a SWLD inaccordance with the presence/absence of the flag.

Also, an encoding method used by WLD encoder 404 to encode a WLD may bedifferent from an encoding method used by SWLD encoder 405 to encode aSWLD.

For example, data of a SWLD is decimated, and thus can have a lowercorrelation with the neighboring data than that of a WLD. For thisreason, of intra prediction and inter prediction, inter prediction maybe more preferentially performed in an encoding method used for a SWLDthan in an encoding method used for a WLD.

Also, an encoding method used for a SWLD and an encoding method used fora WLD may represent three-dimensional positions differently. Forexample, three-dimensional coordinates may be used to represent thethree-dimensional positions of FVXLs in a SWLD and an octree describedbelow may be used to represent three-dimensional positions in a WLD, andvice versa.

Also, SWLD encoder 405 performs encoding in a manner that encodedthree-dimensional data 414 of a SWLD has a smaller data size than thedata size of encoded three-dimensional data 413 of a WLD. A SWLD canhave a lower inter-data correlation, for example, than that of a WLD asdescribed above. This can lead to a decreased encoding efficiency, andthus to encoded three-dimensional data 414 having a larger data sizethan the data size of encoded three-dimensional data 413 of a WLD. Whenthe data size of the resulting encoded three-dimensional data 414 islarger than the data size of encoded three-dimensional data 413 of aWLD, SWLD encoder 405 performs encoding again to re-generate encodedthree-dimensional data 414 having a reduced data size.

For example, SWLD extractor 403 re-generates extracted three-dimensionaldata 412 having a reduced number of keypoints to be extracted, and SWLDencoder 405 encodes such extracted three-dimensional data 412.Alternatively, SWLD encoder 405 may perform more coarse quantization.More coarse quantization is achieved, for example, by rounding the datain the lowermost level in an octree structure described below.

When failing to decrease the data size of encoded three-dimensional data414 of the SWLD to smaller than the data size of encodedthree-dimensional data 413 of the WLD, SWLD encoder 405 may not generateencoded three-dimensional data 414 of the SWLD. Alternatively, encodedthree-dimensional data 413 of the WLD may be copied as encodedthree-dimensional data 414 of the SWLD. Stated differently, encodedthree-dimensional data 413 of the WLD may be used as it is as encodedthree-dimensional data 414 of the SWLD.

Next, the structure and the operation flow of the three-dimensional datadecoding device (e.g., a client) according to the present embodimentwill be described. FIG. 18 is a block diagram of three-dimensional datadecoding device 500 according to the present embodiment. FIG. 19 is aflowchart of three-dimensional data decoding processes performed bythree-dimensional data decoding device 500.

Three-dimensional data decoding device 500 shown in FIG. 18 decodesencoded three-dimensional data 511, thereby generating decodedthree-dimensional data 512 or decoded three-dimensional data 513.Encoded three-dimensional data 511 here is, for example, encodedthree-dimensional data 413 or encoded three-dimensional data 414generated by three-dimensional data encoding device 400.

Such three-dimensional data decoding device 500 includes obtainer 501,header analyzer 502, WLD decoder 503, and SWLD decoder 504.

First, as FIG. 19 shows, obtainer 501 obtains encoded three-dimensionaldata 511 (S501). Next, header analyzer 502 analyzes the header ofencoded three-dimensional data 511 to identify whether encodedthree-dimensional data 511 is a stream including a WLD or a streamincluding a SWLD (S502). For example, the above-described parameterworld_type is referred to in making such identification.

When encoded three-dimensional data 511 is a stream including a WLD (Yesin S503), WLD decoder 503 decodes encoded three-dimensional data 511,thereby generating decoded three-dimensional data 512 of the WLD (S504).Meanwhile, when encoded three-dimensional data 511 is a stream includinga SWLD (No in S503), SWLD decoder 504 decodes encoded three-dimensionaldata 511, thereby generating decoded three-dimensional data 513 of theSWLD (S505).

Also, as in the case of the encoding device, a decoding method used byWLD decoder 503 to decode a WLD may be different from a decoding methodused by SWLD decoder 504 to decode a SWLD. For example, of intraprediction and inter prediction, inter prediction may be morepreferentially performed in a decoding method used for a SWLD than in adecoding method used for a WLD.

Also, a decoding method used for a SWLD and a decoding method used for aWLD may represent three-dimensional positions differently. For example,three-dimensional coordinates may be used to represent thethree-dimensional positions of FVXLs in a SWLD and an octree describedbelow may be used to represent three-dimensional positions in a WLD, andvice versa.

Next, an octree representation will be described, which is a method ofrepresenting three-dimensional positions. VXL data included inthree-dimensional data is converted into an octree structure beforeencoded. FIG. 20 is a diagram showing example VXLs in a WLD. FIG. 21 isa diagram showing an octree structure of the WLD shown in FIG. 20 . Anexample shown in FIG. 20 illustrates three VXLs 1 to 3 that includepoint clouds (hereinafter referred to as effective VXLs). As FIG. 21shows, the octree structure is made of nodes and leaves. Each node has amaximum of eight nodes or leaves. Each leaf has VXL information. Here,of the leaves shown in FIG. 21 , leaf 1, leaf 2, and leaf 3 representVXL1, VXL2, and VXL3 shown in FIG. 20 , respectively.

More specifically, each node and each leaf correspond to athree-dimensional position. Node 1 corresponds to the entire block shownin FIG. 20 . The block that corresponds to node 1 is divided into eightblocks. Of these eight blocks, blocks including effective VXLs are setas nodes, while the other blocks are set as leaves. Each block thatcorresponds to a node is further divided into eight nodes or leaves.These processes are repeated by the number of times that is equal to thenumber of levels in the octree structure. All blocks in the lowermostlevel are set as leaves.

FIG. 22 is a diagram showing an example SWLD generated from the WLDshown in FIG. 20 . VXL1 and VXL2 shown in FIG. 20 are judged as FVXL1and FVXL2 as a result of feature extraction, and thus are added to theSWLD. Meanwhile, VXL3 is not judged as a FVXL, and thus is not added tothe SWLD. FIG. 23 is a diagram showing an octree structure of the SWLDshown in FIG. 22 . In the octree structure shown in FIG. 23 , leaf 3corresponding to VXL3 shown in FIG. 21 is deleted. Consequently, node 3shown in FIG. 21 has lost an effective VXL, and has changed to a leaf.As described above, a SWLD has a smaller number of leaves in generalthan a WLD does, and thus the encoded three-dimensional data of the SWLDis smaller than the encoded three-dimensional data of the WLD.

The following describes variations of the present embodiment.

For self-location estimation, for example, a client, being avehicle-mounted device, etc., may receive a SWLD from the server to usesuch SWLD to estimate the self-location. Meanwhile, for obstacledetection, the client may detect obstacles by use of three-dimensionalinformation on the periphery obtained by such client through variousmeans including a distance sensor such as a rangefinder, as well as astereo camera and a combination of a plurality of monocular cameras.

In general, a SWLD is less likely to include VXL data on a flat region.As such, the server may hold a subsample world (subWLD) obtained bysubsampling a WLD for detection of static obstacles, and send to theclient the SWLD and the subWLD. This enables the client to performself-location estimation and obstacle detection on the client's part,while reducing the network bandwidth.

When the client renders three-dimensional map data at a high speed, mapinformation having a mesh structure is more useful in some cases. Assuch, the server may generate a mesh from a WLD to hold it beforehand asa mesh world (MWLD). For example, when wishing to perform coarsethree-dimensional rendering, the client receives a MWLD, and whenwishing to perform detailed three-dimensional rendering, the clientreceives a WLD. This reduces the network bandwidth.

In the above description, the server sets, as FVXLs, VXLs having anamount of features greater than or equal to the threshold, but theserver may calculate FVXLs by a different method. For example, theserver may judge that a VXL, a VLM, a SPC, or a GOS that constitutes asignal, or an intersection, etc. as necessary for self-locationestimation, driving assist, or self-driving, etc., and incorporate suchVXL, VLM, SPC, or GOS into a SWLD as a FVXL, a FVLM, a FSPC, or a FGOS.Such judgment may be made manually. Also, FVXLs, etc. that have been seton the basis of an amount of features may be added to FVXLs, etc.obtained by the above method. Stated differently, SWLD extractor 403 mayfurther extract, from input three-dimensional data 411, datacorresponding to an object having a predetermined attribute as extractedthree-dimensional data 412.

Also, that a VXL, a VLM, a SPC, or a GOS is necessary for such intendedusage may be labeled separately from the features. The server mayseparately hold, as an upper layer of a SWLD (e.g., a lane world), FVXLsof a signal or an intersection, etc. necessary for self-locationestimation, driving assist, or self-driving, etc.

The server may also add an attribute to VXLs in a WLD on a random accessbasis or on a predetermined unit basis. An attribute, for example,includes information indicating whether VXLs are necessary forself-location estimation, or information indicating whether VXLs areimportant as traffic information such as a signal, or an intersection,etc. An attribute may also include a correspondence between VXLs andfeatures (intersection, or road, etc.) in lane information (geographicdata files (GDF), etc.).

A method as described below may be used to update a WLD or a SWLD.

Update information indicating changes, etc. in a person, a roadwork, ora tree line (for trucks) is uploaded to the server as point clouds ormeta data. The server updates a WLD on the basis of such uploadedinformation, and then updates a SWLD by use of the updated WLD.

The client, when detecting a mismatch between the three-dimensionalinformation such client has generated at the time of self-locationestimation and the three-dimensional information received from theserver, may send to the server the three-dimensional information suchclient has generated, together with an update notification. In such acase, the server updates the SWLD by use of the WLD. When the SWLD isnot to be updated, the server judges that the WLD itself is old.

In the above description, information that distinguishes whether anencoded stream is that of a WLD or a SWLD is added as header informationof the encoded stream. However, when there are many types of worlds suchas a mesh world and a lane world, information that distinguishes thesetypes of the worlds may be added to header information. Also, when thereare many SWLDs with different amounts of features, information thatdistinguishes the respective SWLDs may be added to header information.

In the above description, a SWLD is constituted by FVXLs, but a SWLD mayinclude VXLs that have not been judged as FVXLs. For example, a SWLD mayinclude an adjacent VXL used to calculate the feature of a FVXL. Thisenables the client to calculate the feature of a FVXL when receiving aSWLD, even in the case where feature information is not added to eachFVXL of the SWLD. In such a case, the SWLD may include information thatdistinguishes whether each VXL is a FVXL or a VXL.

As described above, three-dimensional data encoding device 400 extracts,from input three-dimensional data 411 (first three-dimensional data),extracted three-dimensional data 412 (second three-dimensional data)having an amount of a feature greater than or equal to a threshold, andencodes extracted three-dimensional data 412 to generate encodedthree-dimensional data 414 (first encoded three-dimensional data).

This three-dimensional data encoding device 400 generates encodedthree-dimensional data 414 that is obtained by encoding data having anamount of a feature greater than or equal to the threshold. This reducesthe amount of data compared to the case where input three-dimensionaldata 411 is encoded as it is. Three-dimensional data encoding device 400is thus capable of reducing the amount of data to be transmitted.

Three-dimensional data encoding device 400 further encodes inputthree-dimensional data 411 to generate encoded three-dimensional data413 (second encoded three-dimensional data).

This three-dimensional data encoding device 400 enables selectivetransmission of encoded three-dimensional data 413 and encodedthree-dimensional data 414, in accordance, for example, with theintended use, etc.

Also, extracted three-dimensional data 412 is encoded by a firstencoding method, and input three-dimensional data 411 is encoded by asecond encoding method different from the first encoding method.

This three-dimensional data encoding device 400 enables the use of anencoding method suitable for each of input three-dimensional data 411and extracted three-dimensional data 412.

Also, of intra prediction and inter prediction, the inter prediction ismore preferentially performed in the first encoding method than in thesecond encoding method.

This three-dimensional data encoding device 400 enables inter predictionto be more preferentially performed on extracted three-dimensional data412 in which adjacent data items are likely to have low correlation.

Also, the first encoding method and the second encoding method representthree-dimensional positions differently. For example, the secondencoding method represents three-dimensional positions by octree, andthe first encoding method represents three-dimensional positions bythree-dimensional coordinates.

This three-dimensional data encoding device 400 enables the use of amore suitable method to represent the three-dimensional positions ofthree-dimensional data in consideration of the difference in the numberof data items (the number of VXLs or FVXLs) included.

Also, at least one of encoded three-dimensional data 413 and encodedthree-dimensional data 414 includes an identifier indicating whether theencoded three-dimensional data is encoded three-dimensional dataobtained by encoding input three-dimensional data 411 or encodedthree-dimensional data obtained by encoding part of inputthree-dimensional data 411. Stated differently, such identifierindicates whether the encoded three-dimensional data is encodedthree-dimensional data 413 of a WLD or encoded three-dimensional data414 of a SWLD.

This enables the decoding device to readily judge whether the obtainedencoded three-dimensional data is encoded three-dimensional data 413 orencoded three-dimensional data 414.

Also, three-dimensional data encoding device 400 encodes extractedthree-dimensional data 412 in a manner that encoded three-dimensionaldata 414 has a smaller data amount than a data amount of encodedthree-dimensional data 413.

This three-dimensional data encoding device 400 enables encodedthree-dimensional data 414 to have a smaller data amount than the dataamount of encoded three-dimensional data 413.

Also, three-dimensional data encoding device 400 further extracts datacorresponding to an object having a predetermined attribute from inputthree-dimensional data 411 as extracted three-dimensional data 412. Theobject having a predetermined attribute is, for example, an objectnecessary for self-location estimation, driving assist, or self-driving,etc., or more specifically, a signal, an intersection, etc.

This three-dimensional data encoding device 400 is capable of generatingencoded three-dimensional data 414 that includes data required by thedecoding device.

Also, three-dimensional data encoding device 400 (server) further sends,to a client, one of encoded three-dimensional data 413 and encodedthree-dimensional data 414 in accordance with a status of the client.

This three-dimensional data encoding device 400 is capable of sendingappropriate data in accordance with the status of the client.

Also, the status of the client includes one of a communication condition(e.g., network bandwidth) of the client and a traveling speed of theclient.

Also, three-dimensional data encoding device 400 further sends, to aclient, one of encoded three-dimensional data 413 and encodedthree-dimensional data 414 in accordance with a request from the client.

This three-dimensional data encoding device 400 is capable of sendingappropriate data in accordance with the request from the client.

Also, three-dimensional data decoding device 500 according to thepresent embodiment decodes encoded three-dimensional data 413 or encodedthree-dimensional data 414 generated by three-dimensional data encodingdevice 400 described above.

Stated differently, three-dimensional data decoding device 500 decodes,by a first decoding method, encoded three-dimensional data 414 obtainedby encoding extracted three-dimensional data 412 having an amount of afeature greater than or equal to a threshold, extractedthree-dimensional data 412 having been extracted from inputthree-dimensional data 411. Three-dimensional data decoding device 500also decodes, by a second decoding method, encoded three-dimensionaldata 413 obtained by encoding input three-dimensional data 411, thesecond decoding method being different from the first decoding method.

This three-dimensional data decoding device 500 enables selectivereception of encoded three-dimensional data 414 obtained by encodingdata having an amount of a feature greater than or equal to thethreshold and encoded three-dimensional data 413, in accordance, forexample, with the intended use, etc. Three-dimensional data decodingdevice 500 is thus capable of reducing the amount of data to betransmitted. Such three-dimensional data decoding device 500 furtherenables the use of a decoding method suitable for each of inputthree-dimensional data 411 and extracted three-dimensional data 412.

Also, of intra prediction and inter prediction, the inter prediction ismore preferentially performed in the first decoding method than in thesecond decoding method.

This three-dimensional data decoding device 500 enables inter predictionto be more preferentially performed on the extracted three-dimensionaldata in which adjacent data items are likely to have low correlation.

Also, the first decoding method and the second decoding method representthree-dimensional positions differently. For example, the seconddecoding method represents three-dimensional positions by octree, andthe first decoding method represents three-dimensional positions bythree-dimensional coordinates.

This three-dimensional data decoding device 500 enables the use of amore suitable method to represent the three-dimensional positions ofthree-dimensional data in consideration of the difference in the numberof data items (the number of VXLs or FVXLs) included.

Also, at least one of encoded three-dimensional data 413 and encodedthree-dimensional data 414 includes an identifier indicating whether theencoded three-dimensional data is encoded three-dimensional dataobtained by encoding input three-dimensional data 411 or encodedthree-dimensional data obtained by encoding part of inputthree-dimensional data 411. Three-dimensional data decoding device 500refers to such identifier in identifying between encodedthree-dimensional data 413 and encoded three-dimensional data 414.

This three-dimensional data decoding device 500 is capable of readilyjudging whether the obtained encoded three-dimensional data is encodedthree-dimensional data 413 or encoded three-dimensional data 414.

Three-dimensional data decoding device 500 further notifies a server ofa status of the client (three-dimensional data decoding device 500).Three-dimensional data decoding device 500 receives one of encodedthree-dimensional data 413 and encoded three-dimensional data 414 fromthe server, in accordance with the status of the client.

This three-dimensional data decoding device 500 is capable of receivingappropriate data in accordance with the status of the client.

Also, the status of the client includes one of a communication condition(e.g., network bandwidth) of the client and a traveling speed of theclient.

Three-dimensional data decoding device 500 further makes a request ofthe server for one of encoded three-dimensional data 413 and encodedthree-dimensional data 414, and receives one of encodedthree-dimensional data 413 and encoded three-dimensional data 414 fromthe server, in accordance with the request.

This three-dimensional data decoding device 500 is capable of receivingappropriate data in accordance with the intended use.

Embodiment 3

The present embodiment will describe a method of transmitting/receivingthree-dimensional data between vehicles. For example, thethree-dimensional data is transmitted/received between the own vehicleand the nearby vehicle.

FIG. 24 is a block diagram of three-dimensional data creation device 620according to the present embodiment. Such three-dimensional datacreation device 620, which is included, for example, in the own vehicle,mergers first three-dimensional data 632 created by three-dimensionaldata creation device 620 with the received second three-dimensional data635, thereby creating third three-dimensional data 636 having a higherdensity.

Such three-dimensional data creation device 620 includesthree-dimensional data creator 621, request range determiner 622,searcher 623, receiver 624, decoder 625, and merger 626.

First, three-dimensional data creator 621 creates firstthree-dimensional data 632 by use of sensor information 631 detected bythe sensor included in the own vehicle. Next, request range determiner622 determines a request range, which is the range of athree-dimensional space, the data on which is insufficient in thecreated first three-dimensional data 632.

Next, searcher 623 searches for the nearby vehicle having thethree-dimensional data of the request range, and sends request rangeinformation 633 indicating the request range to nearby vehicle 601having been searched out (S623). Next, receiver 624 receives encodedthree-dimensional data 634, which is an encoded stream of the requestrange, from nearby vehicle 601 (S624). Note that searcher 623 mayindiscriminately send requests to all vehicles included in a specifiedrange to receive encoded three-dimensional data 634 from a vehicle thathas responded to the request. Searcher 623 may send a request not onlyto vehicles but also to an object such as a signal and a sign, andreceive encoded three-dimensional data 634 from the object.

Next, decoder 625 decodes the received encoded three-dimensional data634, thereby obtaining second three-dimensional data 635. Next, merger626 merges first three-dimensional data 632 with secondthree-dimensional data 635, thereby creating three-dimensional data 636having a higher density.

Next, the structure and operations of three-dimensional datatransmission device 640 according to the present embodiment will bedescribed. FIG. 25 is a block diagram of three-dimensional datatransmission device 640.

Three-dimensional data transmission device 640 is included, for example,in the above-described nearby vehicle. Three-dimensional datatransmission device 640 processes fifth three-dimensional data 652created by the nearby vehicle into sixth three-dimensional data 654requested by the own vehicle, encodes sixth three-dimensional data 654to generate encoded three-dimensional data 634, and sends encodedthree-dimensional data 634 to the own vehicle.

Three-dimensional data transmission device 640 includesthree-dimensional data creator 641, receiver 642, extractor 643, encoder644, and transmitter 645.

First, three-dimensional data creator 641 creates fifththree-dimensional data 652 by use of sensor information 651 detected bythe sensor included in the nearby vehicle. Next, receiver 642 receivesrequest range information 633 from the own vehicle.

Next, extractor 643 extracts from fifth three-dimensional data 652 thethree-dimensional data of the request range indicated by request rangeinformation 633, thereby processing fifth three-dimensional data 652into sixth three-dimensional data 654. Next, encoder 644 encodes sixththree-dimensional data 654 to generate encoded three-dimensional data643, which is an encoded stream. Then, transmitter 645 sends encodedthree-dimensional data 634 to the own vehicle.

Note that although an example case is described here in which the ownvehicle includes three-dimensional data creation device 620 and thenearby vehicle includes three-dimensional data transmission device 640,each of the vehicles may include the functionality of boththree-dimensional data creation device 620 and three-dimensional datatransmission device 640.

Embodiment 4

The present embodiment describes operations performed in abnormal caseswhen self-location estimation is performed on the basis of athree-dimensional map.

A three-dimensional map is expected to find its expanded use inself-driving of a vehicle and autonomous movement, etc. of a mobileobject such as a robot and a flying object (e.g., a drone). Examplemeans for enabling such autonomous movement include a method in which amobile object travels in accordance with a three-dimensional map, whileestimating its self-location on the map (self-location estimation).

The self-location estimation is enabled by matching a three-dimensionalmap with three-dimensional information on the surrounding of the ownvehicle (hereinafter referred to as self-detected three-dimensionaldata) obtained by a sensor equipped in the own vehicle, such as arangefinder (e.g., a LiDAR) and a stereo camera to estimate the locationof the own vehicle on the three-dimensional map.

As in the case of an HD map suggested by HERE Technologies, for example,a three-dimensional map may include not only a three-dimensional pointcloud, but also two-dimensional map data such as information on theshapes of roads and intersections, or information that changes inreal-time such as information on a traffic jam and an accident. Athree-dimensional map includes a plurality of layers such as layers ofthree-dimensional data, two-dimensional data, and meta-data that changesin real-time, from among which the device can obtain or refer to onlynecessary data.

Point cloud data may be a SWLD as described above, or may include pointcloud data that is different from keypoints. The transmission/receptionof point cloud data is basically carried out in one or more randomaccess units.

A method described below is used as a method of matching athree-dimensional map with self-detected three-dimensional data. Forexample, the device compares the shapes of the point clouds in eachother's point clouds, and determines that portions having a high degreeof similarity among keypoints correspond to the same position. When thethree-dimensional map is formed by a SWLD, the device also performsmatching by comparing the keypoints that form the SWLD withthree-dimensional keypoints extracted from the self-detectedthree-dimensional data.

Here, to enable highly accurate self-location estimation, the followingneeds to be satisfied: (A) the three-dimensional map and theself-detected three-dimensional data have been already obtained; and (B)their accuracies satisfy a predetermined requirement. However, one of(A) and (B) cannot be satisfied in abnormal cases such as ones describedbelow.

-   -   1. A three-dimensional map is unobtainable over communication.    -   2. A three-dimensional map is not present, or a        three-dimensional map having been obtained is corrupt.    -   3. A sensor of the own vehicle has trouble, or the accuracy of        the generated self-detected three-dimensional data is inadequate        due to bad weather.

The following describes operations to cope with such abnormal cases. Thefollowing description illustrates an example case of a vehicle, but themethod described below is applicable to mobile objects on the whole thatare capable of autonomous movement, such as a robot and a drone.

The following describes the structure of the three-dimensionalinformation processing device and its operation according to the presentembodiment capable of coping with abnormal cases regarding athree-dimensional map or self-detected three-dimensional data. FIG. 26is a block diagram of an example structure of three-dimensionalinformation processing device 700 according to the present embodiment.

Three-dimensional information processing device 700 is equipped, forexample, in a mobile object such as a car. As shown in FIG. 26 ,three-dimensional information processing device 700 includesthree-dimensional map obtainer 701, self-detected data obtainer 702,abnormal case judgment unit 703, coping operation determiner 704, andoperation controller 705.

Note that three-dimensional information processing device 700 mayinclude a non-illustrated two-dimensional or one-dimensional sensor thatdetects a structural object or a mobile object around the own vehicle,such as a camera capable of obtaining two-dimensional images and asensor for one-dimensional data utilizing ultrasonic or laser.Three-dimensional information processing device 700 may also include anon-illustrated communication unit that obtains a three-dimensional mapover a mobile communication network, such as 4G and 5G, or viainter-vehicle communication or road-to-vehicle communication.

Three-dimensional map obtainer 701 obtains three-dimensional map 711 ofthe surroundings of the traveling route. For example, three-dimensionalmap obtainer 701 obtains three-dimensional map 711 over a mobilecommunication network, or via inter-vehicle communication orroad-to-vehicle communication.

Next, self-detected data obtainer 702 obtains self-detectedthree-dimensional data 712 on the basis of sensor information. Forexample, self-detected data obtainer 702 generates self-detectedthree-dimensional data 712 on the basis of the sensor informationobtained by a sensor equipped in the own vehicle.

Next, abnormal case judgment unit 703 conducts a predetermined check ofat least one of obtained three-dimensional map 711 and self-detectedthree-dimensional data 712 to detect an abnormal case. Stateddifferently, abnormal case judgment unit 703 judges whether at least oneof obtained three-dimensional map 711 and self-detectedthree-dimensional data 712 is abnormal.

When the abnormal case is detected, coping operation determiner 704determines a coping operation to cope with such abnormal case. Next,operation controller 705 controls the operation of each of theprocessing units necessary to perform the coping operation.

Meanwhile, when no abnormal case is detected, three-dimensionalinformation processing device 700 terminates the process.

Also, three-dimensional information processing device 700 estimates thelocation of the vehicle equipped with three-dimensional informationprocessing device 700, using three-dimensional map 711 and self-detectedthree-dimensional data 712. Next, three-dimensional informationprocessing device 700 performs the automatic operation of the vehicle byuse of the estimated location of the vehicle.

As described above, three-dimensional information processing device 700obtains, via a communication channel, map data (three-dimensional map711) that includes first three-dimensional position information. Thefirst three-dimensional position information includes, for example, aplurality of random access units, each of which is an assembly of atleast one subspace and is individually decodable, the at least onesubspace having three-dimensional coordinates information and serving asa unit in which each of the plurality of random access units is encoded.The first three-dimensional position information is, for example, data(SWLD) obtained by encoding keypoints, each of which has an amount of athree-dimensional feature greater than or equal to a predeterminedthreshold.

Three-dimensional information processing device 700 also generatessecond three-dimensional position information (self-detectedthree-dimensional data 712) from information detected by a sensor.Three-dimensional information processing device 700 then judges whetherone of the first three-dimensional position information and the secondthree-dimensional position information is abnormal by performing, on oneof the first three-dimensional position information and the secondthree-dimensional position information, a process of judging whether anabnormality is present.

Three-dimensional information processing device 700 determines a copingoperation to cope with the abnormality when one of the firstthree-dimensional position information and the second three-dimensionalposition information is judged to be abnormal. Three-dimensionalinformation processing device 700 then executes a control that isrequired to perform the coping operation.

This structure enables three-dimensional information processing device700 to detect an abnormality regarding one of the firstthree-dimensional position information and the second three-dimensionalposition information, and to perform a coping operation therefor.

Embodiment 5

The present embodiment describes a method, etc. of transmittingthree-dimensional data to a following vehicle.

FIG. 27 is a block diagram of an exemplary structure ofthree-dimensional data creation device 810 according to the presentembodiment. Such three-dimensional data creation device 810 is equipped,for example, in a vehicle. Three-dimensional data creation device 810transmits and receives three-dimensional data to and from an externalcloud-based traffic monitoring system, a preceding vehicle, or afollowing vehicle, and creates and stores three-dimensional data.

Three-dimensional data creation device 810 includes data receiver 811,communication unit 812, reception controller 813, format converter 814,a plurality of sensors 815, three-dimensional data creator 816,three-dimensional data synthesizer 817, three-dimensional data storage818, communication unit 819, transmission controller 820, formatconverter 821, and data transmitter 822.

Data receiver 811 receives three-dimensional data 831 from a cloud-basedtraffic monitoring system or a preceding vehicle. Three-dimensional data831 includes, for example, information on a region undetectable bysensors 815 of the own vehicle, such as a point cloud, visible lightvideo, depth information, sensor position information, and speedinformation.

Communication unit 812 communicates with the cloud-based trafficmonitoring system or the preceding vehicle to transmit a datatransmission request, etc. to the cloud-based traffic monitoring systemor the preceding vehicle.

Reception controller 813 exchanges information, such as information onsupported formats, with a communications partner via communication unit812 to establish communication with the communications partner.

Format converter 814 applies format conversion, etc. onthree-dimensional data 831 received by data receiver 811 to generatethree-dimensional data 832. Format converter 814 also decompresses ordecodes three-dimensional data 831 when three-dimensional data 831 iscompressed or encoded.

A plurality of sensors 815 are a group of sensors, such as visible lightcameras and infrared cameras, that obtain information on the outside ofthe vehicle and generate sensor information 833. Sensor information 833is, for example, three-dimensional data such as a point cloud (pointcloud data), when sensors 815 are laser sensors such as LiDARs. Notethat a single sensor may serve as a plurality of sensors 815.

Three-dimensional data creator 816 generates three-dimensional data 834from sensor information 833. Three-dimensional data 834 includes, forexample, information such as a point cloud, visible light video, depthinformation, sensor position information, and speed information.

Three-dimensional data synthesizer 817 synthesizes three-dimensionaldata 834 created on the basis of sensor information 833 of the ownvehicle with three-dimensional data 832 created by the cloud-basedtraffic monitoring system or the preceding vehicle, etc., therebyforming three-dimensional data 835 of a space that includes the spaceahead of the preceding vehicle undetectable by sensors 815 of the ownvehicle.

Three-dimensional data storage 818 stores generated three-dimensionaldata 835, etc.

Communication unit 819 communicates with the cloud-based trafficmonitoring system or the following vehicle to transmit a datatransmission request, etc. to the cloud-based traffic monitoring systemor the following vehicle.

Transmission controller 820 exchanges information such as information onsupported formats with a communications partner via communication unit819 to establish communication with the communications partner.Transmission controller 820 also determines a transmission region, whichis a space of the three-dimensional data to be transmitted, on the basisof three-dimensional data formation information on three-dimensionaldata 832 generated by three-dimensional data synthesizer 817 and thedata transmission request from the communications partner.

More specifically, transmission controller 820 determines a transmissionregion that includes the space ahead of the own vehicle undetectable bya sensor of the following vehicle, in response to the data transmissionrequest from the cloud-based traffic monitoring system or the followingvehicle. Transmission controller 820 judges, for example, whether aspace is transmittable or whether the already transmitted space includesan update, on the basis of the three-dimensional data formationinformation to determine a transmission region. For example,transmission controller 820 determines, as a transmission region, aregion that is: a region specified by the data transmission request; anda region, corresponding three-dimensional data 835 of which is present.Transmission controller 820 then notifies format converter 821 of theformat supported by the communications partner and the transmissionregion.

Of three-dimensional data 835 stored in three-dimensional data storage818, format converter 821 converts three-dimensional data 836 of thetransmission region into the format supported by the receiver end togenerate three-dimensional data 837. Note that format converter 821 maycompress or encode three-dimensional data 837 to reduce the data amount.

Data transmitter 822 transmits three-dimensional data 837 to thecloud-based traffic monitoring system or the following vehicle. Suchthree-dimensional data 837 includes, for example, information on a blindspot, which is a region hidden from view of the following vehicle, suchas a point cloud ahead of the own vehicle, visible light video, depthinformation, and sensor position information.

Note that an example has been described in which format converter 814and format converter 821 perform format conversion, etc., but formatconversion may not be performed.

With the above structure, three-dimensional data creation device 810obtains, from an external device, three-dimensional data 831 of a regionundetectable by sensors 815 of the own vehicle, and synthesizesthree-dimensional data 831 with three-dimensional data 834 that is basedon sensor information 833 detected by sensors 815 of the own vehicle,thereby generating three-dimensional data 835. Three-dimensional datacreation device 810 is thus capable of generating three-dimensional dataof a range undetectable by sensors 815 of the own vehicle.

Three-dimensional data creation device 810 is also capable oftransmitting, to the cloud-based traffic monitoring system or thefollowing vehicle, etc., three-dimensional data of a space that includesthe space ahead of the own vehicle undetectable by a sensor of thefollowing vehicle, in response to the data transmission request from thecloud-based traffic monitoring system or the following vehicle.

Embodiment 6

In embodiment 5, an example is described in which a client device of avehicle or the like transmits three-dimensional data to another vehicleor a server such as a cloud-based traffic monitoring system. In thepresent embodiment, a client device transmits sensor informationobtained through a sensor to a server or a client device.

A structure of a system according to the present embodiment will firstbe described. FIG. 28 is a diagram showing the structure of atransmission/reception system of a three-dimensional map and sensorinformation according to the present embodiment. This system includesserver 901, and client devices 902A and 902B. Note that client devices902A and 902B are also referred to as client device 902 when noparticular distinction is made therebetween.

Client device 902 is, for example, a vehicle-mounted device equipped ina mobile object such as a vehicle. Server 901 is, for example, acloud-based traffic monitoring system, and is capable of communicatingwith the plurality of client devices 902.

Server 901 transmits the three-dimensional map formed by a point cloudto client device 902. Note that a structure of the three-dimensional mapis not limited to a point cloud, and may also be another structureexpressing three-dimensional data such as a mesh structure.

Client device 902 transmits the sensor information obtained by clientdevice 902 to server 901. The sensor information includes, for example,at least one of information obtained by LiDAR, a visible light image, aninfrared image, a depth image, sensor position information, or sensorspeed information.

The data to be transmitted and received between server 901 and clientdevice 902 may be compressed in order to reduce data volume, and mayalso be transmitted uncompressed in order to maintain data precision.When compressing the data, it is possible to use a three-dimensionalcompression method on the point cloud based on, for example, an octreestructure. It is possible to use a two-dimensional image compressionmethod on the visible light image, the infrared image, and the depthimage. The two-dimensional image compression method is, for example,MPEG-4 AVC or HEVC standardized by MPEG.

Server 901 transmits the three-dimensional map managed by server 901 toclient device 902 in response to a transmission request for thethree-dimensional map from client device 902. Note that server 901 mayalso transmit the three-dimensional map without waiting for thetransmission request for the three-dimensional map from client device902. For example, server 901 may broadcast the three-dimensional map toat least one client device 902 located in a predetermined space. Server901 may also transmit the three-dimensional map suited to a position ofclient device 902 at fixed time intervals to client device 902 that hasreceived the transmission request once. Server 901 may also transmit thethree-dimensional map managed by server 901 to client device 902 everytime the three-dimensional map is updated.

Client device 902 sends the transmission request for thethree-dimensional map to server 901. For example, when client device 902wants to perform the self-location estimation during traveling, clientdevice 902 transmits the transmission request for the three-dimensionalmap to server 901.

Note that in the following cases, client device 902 may send thetransmission request for the three-dimensional map to server 901. Clientdevice 902 may send the transmission request for the three-dimensionalmap to server 901 when the three-dimensional map stored by client device902 is old. For example, client device 902 may send the transmissionrequest for the three-dimensional map to server 901 when a fixed periodhas passed since the three-dimensional map is obtained by client device902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 before a fixed time when clientdevice 902 exits a space shown in the three-dimensional map stored byclient device 902. For example, client device 902 may send thetransmission request for the three-dimensional map to server 901 whenclient device 902 is located within a predetermined distance from aboundary of the space shown in the three-dimensional map stored byclient device 902. When a movement path and a movement speed of clientdevice 902 are understood, a time when client device 902 exits the spaceshown in the three-dimensional map stored by client device 902 may bepredicted based on the movement path and the movement speed of clientdevice 902.

Client device 902 may also send the transmission request for thethree-dimensional map to server 901 when an error during alignment ofthe three-dimensional data and the three-dimensional map created fromthe sensor information by client device 902 is at least at a fixedlevel.

Client device 902 transmits the sensor information to server 901 inresponse to a transmission request for the sensor information fromserver 901. Note that client device 902 may transmit the sensorinformation to server 901 without waiting for the transmission requestfor the sensor information from server 901. For example, client device902 may periodically transmit the sensor information during a fixedperiod when client device 902 has received the transmission request forthe sensor information from server 901 once. Client device 902 maydetermine that there is a possibility of a change in thethree-dimensional map of a surrounding area of client device 902 havingoccurred, and transmit this information and the sensor information toserver 901, when the error during alignment of the three-dimensionaldata created by client device 902 based on the sensor information andthe three-dimensional map obtained from server 901 is at least at thefixed level.

Server 901 sends a transmission request for the sensor information toclient device 902. For example, server 901 receives positioninformation, such as GPS information, about client device 902 fromclient device 902. Server 901 sends the transmission request for thesensor information to client device 902 in order to generate a newthree-dimensional map, when it is determined that client device 902 isapproaching a space in which the three-dimensional map managed by server901 contains little information, based on the position information aboutclient device 902. Server 901 may also send the transmission request forthe sensor information, when wanting to (i) update the three-dimensionalmap, (ii) check road conditions during snowfall, a disaster, or thelike, or (iii) check traffic congestion conditions, accident/incidentconditions, or the like.

Client device 902 may set an amount of data of the sensor information tobe transmitted to server 901 in accordance with communication conditionsor bandwidth during reception of the transmission request for the sensorinformation to be received from server 901. Setting the amount of dataof the sensor information to be transmitted to server 901 is, forexample, increasing/reducing the data itself or appropriately selectinga compression method.

FIG. 29 is a block diagram showing an example structure of client device902. Client device 902 receives the three-dimensional map formed by apoint cloud and the like from server 901, and estimates a self-locationof client device 902 using the three-dimensional map created based onthe sensor information of client device 902. Client device 902 transmitsthe obtained sensor information to server 901.

Client device 902 includes data receiver 1011, communication unit 1012,reception controller 1013, format converter 1014, sensors 1015,three-dimensional data creator 1016, three-dimensional image processor1017, three-dimensional data storage 1018, format converter 1019,communication unit 1020, transmission controller 1021, and datatransmitter 1022.

Data receiver 1011 receives three-dimensional map 1031 from server 901.Three-dimensional map 1031 is data that includes a point cloud such as aWLD or a SWLD. Three-dimensional map 1031 may include compressed data oruncompressed data.

Communication unit 1012 communicates with server 901 and transmits adata transmission request (e.g. transmission request forthree-dimensional map) to server 901.

Reception controller 1013 exchanges information, such as information onsupported formats, with a communications partner via communication unit1012 to establish communication with the communications partner.

Format converter 1014 performs a format conversion and the like onthree-dimensional map 1031 received by data receiver 1011 to generatethree-dimensional map 1032. Format converter 1014 also performs adecompression or decoding process when three-dimensional map 1031 iscompressed or encoded. Note that format converter 1014 does not performthe decompression or decoding process when three-dimensional map 1031 isuncompressed data.

Sensors 815 are a group of sensors, such as LiDARs, visible lightcameras, infrared cameras, or depth sensors that obtain informationabout the outside of a vehicle equipped with client device 902, andgenerate sensor information 1033. Sensor information 1033 is, forexample, three-dimensional data such as a point cloud (point cloud data)when sensors 1015 are laser sensors such as LiDARs. Note that a singlesensor may serve as sensors 1015.

Three-dimensional data creator 1016 generates three-dimensional data1034 of a surrounding area of the own vehicle based on sensorinformation 1033. For example, three-dimensional data creator 1016generates point cloud data with color information on the surroundingarea of the own vehicle using information obtained by LiDAR and visiblelight video obtained by a visible light camera.

Three-dimensional image processor 1017 performs a self-locationestimation process and the like of the own vehicle, using (i) thereceived three-dimensional map 1032 such as a point cloud, and (ii)three-dimensional data 1034 of the surrounding area of the own vehiclegenerated using sensor information 1033. Note that three-dimensionalimage processor 1017 may generate three-dimensional data 1035 about thesurroundings of the own vehicle by merging three-dimensional map 1032and three-dimensional data 1034, and may perform the self-locationestimation process using the created three-dimensional data 1035.

Three-dimensional data storage 1018 stores three-dimensional map 1032,three-dimensional data 1034, three-dimensional data 1035, and the like.

Format converter 1019 generates sensor information 1037 by convertingsensor information 1033 to a format supported by a receiver end. Notethat format converter 1019 may reduce the amount of data by compressingor encoding sensor information 1037. Format converter 1019 may omit thisprocess when format conversion is not necessary. Format converter 1019may also control the amount of data to be transmitted in accordance witha specified transmission range.

Communication unit 1020 communicates with server 901 and receives a datatransmission request (transmission request for sensor information) andthe like from server 901.

Transmission controller 1021 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1020 to establish communication with the communications partner.

Data transmitter 1022 transmits sensor information 1037 to server 901.Sensor information 1037 includes, for example, information obtainedthrough sensors 1015, such as information obtained by LiDAR, a luminanceimage obtained by a visible light camera, an infrared image obtained byan infrared camera, a depth image obtained by a depth sensor, sensorposition information, and sensor speed information.

A structure of server 901 will be described next. FIG. 30 is a blockdiagram showing an example structure of server 901. Server 901 transmitssensor information from client device 902 and creates three-dimensionaldata based on the received sensor information. Server 901 updates thethree-dimensional map managed by server 901 using the createdthree-dimensional data. Server 901 transmits the updatedthree-dimensional map to client device 902 in response to a transmissionrequest for the three-dimensional map from client device 902.

Server 901 includes data receiver 1111, communication unit 1112,reception controller 1113, format converter 1114, three-dimensional datacreator 1116, three-dimensional data merger 1117, three-dimensional datastorage 1118, format converter 1119, communication unit 1120,transmission controller 1121, and data transmitter 1122.

Data receiver 1111 receives sensor information 1037 from client device902. Sensor information 1037 includes, for example, information obtainedby LiDAR, a luminance image obtained by a visible light camera, aninfrared image obtained by an infrared camera, a depth image obtained bya depth sensor, sensor position information, sensor speed information,and the like.

Communication unit 1112 communicates with client device 902 andtransmits a data transmission request (e.g. transmission request forsensor information) and the like to client device 902.

Reception controller 1113 exchanges information, such as information onsupported formats, with a communications partner via communication unit1112 to establish communication with the communications partner.

Format converter 1114 generates sensor information 1132 by performing adecompression or decoding process when received sensor information 1037is compressed or encoded. Note that format converter 1114 does notperform the decompression or decoding process when sensor information1037 is uncompressed data.

Three-dimensional data creator 1116 generates three-dimensional data1134 of a surrounding area of client device 902 based on sensorinformation 1132. For example, three-dimensional data creator 1116generates point cloud data with color information on the surroundingarea of client device 902 using information obtained by LiDAR andvisible light video obtained by a visible light camera.

Three-dimensional data merger 1117 updates three-dimensional map 1135 bymerging three-dimensional data 1134 created based on sensor information1132 with three-dimensional map 1135 managed by server 901.

Three-dimensional data storage 1118 stores three-dimensional map 1135and the like.

Format converter 1119 generates three-dimensional map 1031 by convertingthree-dimensional map 1135 to a format supported by the receiver end.Note that format converter 1119 may reduce the amount of data bycompressing or encoding three-dimensional map 1135. Format converter1119 may omit this process when format conversion is not necessary.Format converter 1119 may also control the amount of data to betransmitted in accordance with a specified transmission range.

Communication unit 1120 communicates with client device 902 and receivesa data transmission request (transmission request for three-dimensionalmap) and the like from client device 902.

Transmission controller 1121 exchanges information, such as informationon supported formats, with a communications partner via communicationunit 1120 to establish communication with the communications partner.

Data transmitter 1122 transmits three-dimensional map 1031 to clientdevice 902. Three-dimensional map 1031 is data that includes a pointcloud such as a WLD or a SWLD. Three-dimensional map 1031 may includeone of compressed data and uncompressed data.

An operational flow of client device 902 will be described next. FIG. 31is a flowchart of an operation when client device 902 obtains thethree-dimensional map.

Client device 902 first requests server 901 to transmit thethree-dimensional map (point cloud, etc.) (S1001). At this point, byalso transmitting the position information about client device 902obtained through GPS and the like, client device 902 may also requestserver 901 to transmit a three-dimensional map relating to this positioninformation.

Client device 902 next receives the three-dimensional map from server901 (S1002). When the received three-dimensional map is compressed data,client device 902 decodes the received three-dimensional map andgenerates an uncompressed three-dimensional map (S1003).

Client device 902 next creates three-dimensional data 1034 of thesurrounding area of client device 902 using sensor information 1033obtained by sensors 1015 (S1004). Client device 902 next estimates theself-location of client device 902 using three-dimensional map 1032received from server 901 and three-dimensional data 1034 created usingsensor information 1033 (S1005).

FIG. 32 is a flowchart of an operation when client device 902 transmitsthe sensor information. Client device 902 first receives a transmissionrequest for the sensor information from server 901 (S1011). Clientdevice 902 that has received the transmission request transmits sensorinformation 1037 to server 901 (S1012). Note that client device 902 maygenerate sensor information 1037 by compressing each piece ofinformation using a compression method suited to each piece ofinformation, when sensor information 1033 includes a plurality of piecesof information obtained by sensors 1015.

An operational flow of server 901 will be described next. FIG. 33 is aflowchart of an operation when server 901 obtains the sensorinformation. Server 901 first requests client device 902 to transmit thesensor information (S1021). Server 901 next receives sensor information1037 transmitted from client device 902 in accordance with the request(S1022). Server 901 next creates three-dimensional data 1134 using thereceived sensor information 1037 (S1023). Server 901 next reflects thecreated three-dimensional data 1134 in three-dimensional map 1135(S1024).

FIG. 34 is a flowchart of an operation when server 901 transmits thethree-dimensional map. Server 901 first receives a transmission requestfor the three-dimensional map from client device 902 (S1031). Server 901that has received the transmission request for the three-dimensional maptransmits the three-dimensional map to client device 902 (S1032). Atthis point, server 901 may extract a three-dimensional map of a vicinityof client device 902 along with the position information about clientdevice 902, and transmit the extracted three-dimensional map. Server 901may compress the three-dimensional map formed by a point cloud using,for example, an octree structure compression method, and transmit thecompressed three-dimensional map.

Hereinafter, variations of the present embodiment will be described.

Server 901 creates three-dimensional data 1134 of a vicinity of aposition of client device 902 using sensor information 1037 receivedfrom client device 902. Server 901 next calculates a difference betweenthree-dimensional data 1134 and three-dimensional map 1135, by matchingthe created three-dimensional data 1134 with three-dimensional map 1135of the same area managed by server 901. Server 901 determines that atype of anomaly has occurred in the surrounding area of client device902, when the difference is greater than or equal to a predeterminedthreshold. For example, it is conceivable that a large difference occursbetween three-dimensional map 1135 managed by server 901 andthree-dimensional data 1134 created based on sensor information 1037,when land subsidence and the like occurs due to a natural disaster suchas an earthquake.

Sensor information 1037 may include information indicating at least oneof a sensor type, a sensor performance, and a sensor model number.Sensor information 1037 may also be appended with a class ID and thelike in accordance with the sensor performance. For example, when sensorinformation 1037 is obtained by LiDAR, it is conceivable to assignidentifiers to the sensor performance. A sensor capable of obtaininginformation with precision in units of several millimeters is class 1, asensor capable of obtaining information with precision in units ofseveral centimeters is class 2, and a sensor capable of obtaininginformation with precision in units of several meters is class 3. Server901 may estimate sensor performance information and the like from amodel number of client device 902. For example, when client device 902is equipped in a vehicle, server 901 may determine sensor specificationinformation from a type of the vehicle. In this case, server 901 mayobtain information on the type of the vehicle in advance, and theinformation may also be included in the sensor information. Server 901may change a degree of correction with respect to three-dimensional data1134 created using sensor information 1037, using obtained sensorinformation 1037. For example, when the sensor performance is high inprecision (class 1), server 901 does not correct three-dimensional data1134. When the sensor performance is low in precision (class 3), server901 corrects three-dimensional data 1134 in accordance with theprecision of the sensor. For example, server 901 increases the degree(intensity) of correction with a decrease in the precision of thesensor.

Server 901 may simultaneously send the transmission request for thesensor information to the plurality of client devices 902 in a certainspace. Server 901 does not need to use all of the sensor information forcreating three-dimensional data 1134 and may, for example, select sensorinformation to be used in accordance with the sensor performance, whenhaving received a plurality of pieces of sensor information from theplurality of client devices 902. For example, when updatingthree-dimensional map 1135, server 901 may select high-precision sensorinformation (class 1) from among the received plurality of pieces ofsensor information, and create three-dimensional data 1134 using theselected sensor information.

Server 901 is not limited to only being a server such as a cloud-basedtraffic monitoring system, and may also be another (vehicle-mounted)client device. FIG. 35 is a diagram of a system structure in this case.

For example, client device 902C sends a transmission request for sensorinformation to client device 902A located nearby, and obtains the sensorinformation from client device 902A. Client device 902C then createsthree-dimensional data using the obtained sensor information of clientdevice 902A, and updates a three-dimensional map of client device 902C.This enables client device 902C to generate a three-dimensional map of aspace that can be obtained from client device 902A, and fully utilizethe performance of client device 902C. For example, such a case isconceivable when client device 902C has high performance.

In this case, client device 902A that has provided the sensorinformation is given rights to obtain the high-precisionthree-dimensional map generated by client device 902C. Client device902A receives the high-precision three-dimensional map from clientdevice 902C in accordance with these rights.

Server 901 may send the transmission request for the sensor informationto the plurality of client devices 902 (client device 902A and clientdevice 902B) located nearby client device 902C. When a sensor of clientdevice 902A or client device 902B has high performance, client device902C is capable of creating the three-dimensional data using the sensorinformation obtained by this high-performance sensor.

FIG. 36 is a block diagram showing a functionality structure of server901 and client device 902. Server 901 includes, for example,three-dimensional map compression/decoding processor 1201 thatcompresses and decodes the three-dimensional map and sensor informationcompression/decoding processor 1202 that compresses and decodes thesensor information.

Client device 902 includes three-dimensional map decoding processor 1211and sensor information compression processor 1212. Three-dimensional mapdecoding processor 1211 receives encoded data of the compressedthree-dimensional map, decodes the encoded data, and obtains thethree-dimensional map. Sensor information compression processor 1212compresses the sensor information itself instead of thethree-dimensional data created using the obtained sensor information,and transmits the encoded data of the compressed sensor information toserver 901. With this structure, client device 902 does not need tointernally store a processor that performs a process for compressing thethree-dimensional data of the three-dimensional map (point cloud, etc.),as long as client device 902 internally stores a processor that performsa process for decoding the three-dimensional map (point cloud, etc.).This makes it possible to limit costs, power consumption, and the likeof client device 902.

As stated above, client device 902 according to the present embodimentis equipped in the mobile object, and creates three-dimensional data1034 of a surrounding area of the mobile object using sensor information1033 that is obtained through sensor 1015 equipped in the mobile objectand indicates a surrounding condition of the mobile object. Clientdevice 902 estimates a self-location of the mobile object using thecreated three-dimensional data 1034. Client device 902 transmitsobtained sensor information 1033 to server 901 or another mobile object.

This enables client device 902 to transmit sensor information 1033 toserver 901 or the like. This makes it possible to further reduce theamount of transmission data compared to when transmitting thethree-dimensional data. Since there is no need for client device 902 toperform processes such as compressing or encoding the three-dimensionaldata, it is possible to reduce the processing amount of client device902. As such, client device 902 is capable of reducing the amount ofdata to be transmitted or simplifying the structure of the device.

Client device 902 further transmits the transmission request for thethree-dimensional map to server 901 and receives three-dimensional map1031 from server 901. In the estimating of the self-location, clientdevice 902 estimates the self-location using three-dimensional data 1034and three-dimensional map 1032.

Sensor information 1034 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1033 includes information that indicates aperformance of the sensor.

Client device 902 encodes or compresses sensor information 1033, and inthe transmitting of the sensor information, transmits sensor information1037 that has been encoded or compressed to server 901 or another mobileobject 902. This enables client device 902 to reduce the amount of datato be transmitted.

For example, client device 902 includes a processor and memory. Theprocessor performs the above processes using the memory.

Server 901 according to the present embodiment is capable ofcommunicating with client device 902 equipped in the mobile object, andreceives sensor information 1037 that is obtained through sensor 1015equipped in the mobile object and indicates a surrounding condition ofthe mobile object. Server 901 creates three-dimensional data 1134 of asurrounding area of the mobile object using received sensor information1037.

With this, server 901 creates three-dimensional data 1134 using sensorinformation 1037 transmitted from client device 902. This makes itpossible to further reduce the amount of transmission data compared towhen client device 902 transmits the three-dimensional data. Since thereis no need for client device 902 to perform processes such ascompressing or encoding the three-dimensional data, it is possible toreduce the processing amount of client device 902. As such, server 901is capable of reducing the amount of data to be transmitted orsimplifying the structure of the device.

Server 901 further transmits a transmission request for the sensorinformation to client device 902.

Server 901 further updates three-dimensional map 1135 using the createdthree-dimensional data 1134, and transmits three-dimensional map 1135 toclient device 902 in response to the transmission request forthree-dimensional map 1135 from client device 902.

Sensor information 1037 includes at least one of information obtained bya laser sensor, a luminance image, an infrared image, a depth image,sensor position information, or sensor speed information.

Sensor information 1037 includes information that indicates aperformance of the sensor.

Server 901 further corrects the three-dimensional data in accordancewith the performance of the sensor. This enables the three-dimensionaldata creation method to improve the quality of the three-dimensionaldata.

In the receiving of the sensor information, server 901 receives aplurality of pieces of sensor information 1037 received from a pluralityof client devices 902, and selects sensor information 1037 to be used inthe creating of three-dimensional data 1134, based on a plurality ofpieces of information that each indicates the performance of the sensorincluded in the plurality of pieces of sensor information 1037. Thisenables server 901 to improve the quality of three-dimensional data1134.

Server 901 decodes or decompresses received sensor information 1037, andcreates three-dimensional data 1134 using sensor information 1132 thathas been decoded or decompressed. This enables server 901 to reduce theamount of data to be transmitted.

For example, server 901 includes a processor and memory. The processorperforms the above processes using the memory.

Embodiment 7

In the present embodiment, three-dimensional data encoding and decodingmethods using an inter prediction process will be described.

FIG. 37 is a block diagram of three-dimensional data encoding device1300 according to the present embodiment. This three-dimensional dataencoding device 1300 generates an encoded bitstream (hereinafter, alsosimply referred to as bitstream) that is an encoded signal, by encodingthree-dimensional data. As illustrated in FIG. 37 , three-dimensionaldata encoding device 1300 includes divider 1301, subtractor 1302,transformer 1303, quantizer 1304, inverse quantizer 1305, inversetransformer 1306, adder 1307, reference volume memory 1308, intrapredictor 1309, reference space memory 1310, inter predictor 1311,prediction controller 1312, and entropy encoder 1313.

Divider 1301 divides a plurality of volumes (VLMs) that are encodingunits of each space (SPC) included in the three-dimensional data.Divider 1301 makes an octree representation (make into an octree) ofvoxels in each volume. Note that divider 1301 may make the spaces intoan octree representation with the spaces having the same size as thevolumes. Divider 1301 may also append information (depth information,etc.) necessary for making the octree representation to a header and thelike of a bitstream.

Subtractor 1302 calculates a difference between a volume (encodingtarget volume) outputted by divider 1301 and a predicted volumegenerated through intra prediction or inter prediction, which will bedescribed later, and outputs the calculated difference to transformer1303 as a prediction residual. FIG. 38 is a diagram showing an examplecalculation of the prediction residual. Note that bit sequences of theencoding target volume and the predicted volume shown here are, forexample, position information indicating positions of three-dimensionalpoints included in the volumes.

Hereinafter, a scan order of an octree representation and voxels will bedescribed. A volume is encoded after being converted into an octreestructure (made into an octree). The octree structure includes nodes andleaves. Each node has eight nodes or leaves, and each leaf has voxel(VXL) information. FIG. 39 is a diagram showing an example structure ofa volume including voxels. FIG. 40 is a diagram showing an example ofthe volume shown in FIG. 39 having been converted into the octreestructure. Among the leaves shown in FIG. 40 , leaves 1, 2, and 3respectively represent VXL 1, VXL 2, and VXL 3, and represent VXLsincluding a point cloud (hereinafter, active VXLs).

An octree is represented by, for example, binary sequences of 1s and 0s.For example, when giving the nodes or the active VXLs a value of 1 andeverything else a value of 0, each node and leaf is assigned with thebinary sequence shown in FIG. 40 . Thus, this binary sequence is scannedin accordance with a breadth-first or a depth-first scan order. Forexample, when scanning breadth-first, the binary sequence shown in A ofFIG. 41 is obtained. When scanning depth-first, the binary sequenceshown in B of FIG. 41 is obtained. The binary sequences obtained throughthis scanning are encoded through entropy encoding, which reduces anamount of information.

Depth information in the octree representation will be described next.Depth in the octree representation is used in order to control up to howfine a granularity point cloud information included in a volume isstored. Upon setting a great depth, it is possible to reproduce thepoint cloud information to a more precise level, but an amount of datafor representing the nodes and leaves increases. Upon setting a smalldepth, however, the amount of data decreases, but some information thatthe point cloud information originally held is lost, since pieces ofpoint cloud information including different positions and differentcolors are now considered as pieces of point cloud information includingthe same position and the same color.

For example, FIG. 42 is a diagram showing an example in which the octreewith a depth of 2 shown in FIG. 40 is represented with a depth of 1. Theoctree shown in FIG. 42 has a lower amount of data than the octree shownin FIG. 40 . In other words, the binarized octree shown in FIG. 42 has alower bit count than the octree shown in FIG. 40 . Leaf 1 and leaf 2shown in FIG. 40 are represented by leaf 1 shown in FIG. 41 . In otherwords, the information on leaf 1 and leaf 2 being in different positionsis lost.

FIG. 43 is a diagram showing a volume corresponding to the octree shownin FIG. 42 . VXL 1 and VXL 2 shown in FIG. 39 correspond to VXL 12 shownin FIG. 43 . In this case, three-dimensional data encoding device 1300generates color information of VXL 12 shown in FIG. 43 using colorinformation of VXL 1 and VXL 2 shown in FIG. 39 . For example,three-dimensional data encoding device 1300 calculates an average value,a median, a weighted average value, or the like of the color informationof VXL 1 and VXL 2 as the color information of VXL 12. In this manner,three-dimensional data encoding device 1300 may control a reduction ofthe amount of data by changing the depth of the octree.

Three-dimensional data encoding device 1300 may set the depthinformation of the octree to units of worlds, units of spaces, or unitsof volumes. In this case, three-dimensional data encoding device 1300may append the depth information to header information of the world,header information of the space, or header information of the volume. Inall worlds, spaces, and volumes associated with different times, thesame value may be used as the depth information. In this case,three-dimensional data encoding device 1300 may append the depthinformation to header information managing the worlds associated withall times.

When the color information is included in the voxels, transformer 1303applies frequency transformation, e.g. orthogonal transformation, to aprediction residual of the color information of the voxels in thevolume. For example, transformer 1303 creates a one-dimensional array byscanning the prediction residual in a certain scan order. Subsequently,transformer 1303 transforms the one-dimensional array to a frequencydomain by applying one-dimensional orthogonal transformation to thecreated one-dimensional array. With this, when a value of the predictionresidual in the volume is similar, a value of a low-frequency componentincreases and a value of a high-frequency component decreases. As such,it is possible to more efficiently reduce a code amount in quantizer1304.

Transformer 1303 does not need to use orthogonal transformation in onedimension, but may also use orthogonal transformation in two or moredimensions. For example, transformer 1303 maps the prediction residualto a two-dimensional array in a certain scan order, and appliestwo-dimensional orthogonal transformation to the obtainedtwo-dimensional array. Transformer 1303 may select an orthogonaltransformation method to be used from a plurality of orthogonaltransformation methods. In this case, three-dimensional data encodingdevice 1300 appends, to the bitstream, information indicating whichorthogonal transformation method is used. Transformer 1303 may select anorthogonal transformation method to be used from a plurality oforthogonal transformation methods in different dimensions. In this case,three-dimensional data encoding device 1300 appends, to the bitstream,in how many dimensions the orthogonal transformation method is used.

For example, transformer 1303 matches the scan order of the predictionresidual to a scan order (breadth-first, depth-first, or the like) inthe octree in the volume. This makes it possible to reduce overhead,since information indicating the scan order of the prediction residualdoes not need to be appended to the bitstream. Transformer 1303 mayapply a scan order different from the scan order of the octree. In thiscase, three-dimensional data encoding device 1300 appends, to thebitstream, information indicating the scan order of the predictionresidual. This enables three-dimensional data encoding device 1300 toefficiently encode the prediction residual. Three-dimensional dataencoding device 1300 may append, to the bitstream, information (flag,etc.) indicating whether to apply the scan order of the octree, and mayalso append, to the bitstream, information indicating the scan order ofthe prediction residual when the scan order of the octree is notapplied.

Transformer 1303 does not only transform the prediction residual of thecolor information, and may also transform other attribute informationincluded in the voxels. For example, transformer 1303 may transform andencode information, such as reflectance information, obtained whenobtaining a point cloud through LiDAR and the like.

Transformer 1303 may skip these processes when the spaces do not includeattribute information such as color information. Three-dimensional dataencoding device 1300 may append, to the bitstream, information (flag)indicating whether to skip the processes of transformer 1303.

Quantizer 1304 generates a quantized coefficient by performingquantization using a quantization control parameter on a frequencycomponent of the prediction residual generated by transformer 1303. Withthis, the amount of information is further reduced. The generatedquantized coefficient is outputted to entropy encoder 1313. Quantizer1304 may control the quantization control parameter in units of worlds,units of spaces, or units of volumes. In this case, three-dimensionaldata encoding device 1300 appends the quantization control parameter toeach header information and the like. Quantizer 1304 may performquantization control by changing a weight per frequency component of theprediction residual. For example, quantizer 1304 may precisely quantizea low-frequency component and roughly quantize a high-frequencycomponent. In this case, three-dimensional data encoding device 1300 mayappend, to a header, a parameter expressing a weight of each frequencycomponent.

Quantizer 1304 may skip these processes when the spaces do not includeattribute information such as color information. Three-dimensional dataencoding device 1300 may append, to the bitstream, information (flag)indicating whether to skip the processes of quantizer 1304.

Inverse quantizer 1305 generates an inverse quantized coefficient of theprediction residual by performing inverse quantization on the quantizedcoefficient generated by quantizer 1304 using the quantization controlparameter, and outputs the generated inverse quantized coefficient toinverse transformer 1306.

Inverse transformer 1306 generates an inverse transformation-appliedprediction residual by applying inverse transformation on the inversequantized coefficient generated by inverse quantizer 1305. This inversetransformation-applied prediction residual does not need to completelycoincide with the prediction residual outputted by transformer 1303,since the inverse transformation-applied prediction residual is aprediction residual that is generated after the quantization.

Adder 1307 adds, to generate a reconstructed volume, (i) the inversetransformation-applied prediction residual generated by inversetransformer 1306 to (ii) a predicted volume that is generated throughintra prediction or intra prediction, which will be described later, andis used to generate a pre-quantized prediction residual. Thisreconstructed volume is stored in reference volume memory 1308 orreference space memory 1310.

Intra predictor 1309 generates a predicted volume of an encoding targetvolume using attribute information of a neighboring volume stored inreference volume memory 1308. The attribute information includes colorinformation or a reflectance of the voxels. Intra predictor 1309generates a predicted value of color information or a reflectance of theencoding target volume.

FIG. 44 is a diagram for describing an operation of intra predictor1309. For example, intra predictor 1309 generates the predicted volumeof the encoding target volume (volume idx=3) shown in FIG. 44 , using aneighboring volume (volume idx=0). Volume idx here is identifierinformation that is appended to a volume in a space, and a differentvalue is assigned to each volume. An order of assigning volume idx maybe the same as an encoding order, and may also be different from theencoding order. For example, intra predictor 1309 uses an average valueof color information of voxels included in volume idx=0, which is aneighboring volume, as the predicted value of the color information ofthe encoding target volume shown in FIG. 44 . In this case, a predictionresidual is generated by deducting the predicted value of the colorinformation from the color information of each voxel included in theencoding target volume. The following processes are performed bytransformer 1303 and subsequent processors with respect to thisprediction residual. In this case, three-dimensional data encodingdevice 1300 appends, to the bitstream, neighboring volume informationand prediction mode information. The neighboring volume information hereis information indicating a neighboring volume used in the prediction,and indicates, for example, volume idx of the neighboring volume used inthe prediction. The prediction mode information here indicates a modeused to generate the predicted volume. The mode is, for example, anaverage value mode in which the predicted value is generated using anaverage value of the voxels in the neighboring volume, or a median modein which the predicted value is generated using the median of the voxelsin the neighboring volume.

Intra predictor 1309 may generate the predicted volume using a pluralityof neighboring volumes. For example, in the structure shown in FIG. 44 ,intra predictor 1309 generates predicted volume 0 using a volume withvolume idx=0, and generates predicted volume 1 using a volume withvolume idx=1. Intra predictor 1309 then generates an average ofpredicted volume 0 and predicted volume 1 as a final predicted volume.In this case, three-dimensional data encoding device 1300 may append, tothe bitstream, a plurality of volumes idx of a plurality of volumes usedto generate the predicted volume.

FIG. 45 is a diagram schematically showing the inter prediction processaccording to the present embodiment. Inter predictor 1311 encodes (interpredicts) a space (SPC) associated with certain time T_Cur using anencoded space associated with different time T_LX. In this case, interpredictor 1311 performs an encoding process by applying a rotation andtranslation process to the encoded space associated with different timeT_LX.

Three-dimensional data encoding device 1300 appends, to the bitstream,RT information relating to a rotation and translation process suited tothe space associated with different time T_LX. Different time T_LX is,for example, time T_L0 before certain time T_Cur. At this point,three-dimensional data encoding device 1300 may append, to thebitstream, RT information RT_L0 relating to a rotation and translationprocess suited to a space associated with time T_L0.

Alternatively, different time T_LX is, for example, time T_L1 aftercertain time T_Cur. At this point, three-dimensional data encodingdevice 1300 may append, to the bitstream, RT information RT_L1 relatingto a rotation and translation process suited to a space associated withtime T_L1.

Alternatively, inter predictor 1311 encodes (bidirectional prediction)with reference to the spaces associated with time T_L0 and time T_L1that differ from each other. In this case, three-dimensional dataencoding device 1300 may append, to the bitstream, both RT informationRT_L0 and RT information RT_L1 relating to the rotation and translationprocess suited to the spaces thereof.

Note that T_L0 has been described as being before T_Cur and T_L1 asbeing after T_Cur, but are not necessarily limited thereto. For example,T_L0 and T_L1 may both be before T_Cur. T_L0 and T_L1 may also both beafter T_Cur.

Three-dimensional data encoding device 1300 may append, to thebitstream, RT information relating to a rotation and translation processsuited to spaces associated with different times, when encoding withreference to each of the spaces. For example, three-dimensional dataencoding device 1300 manages a plurality of encoded spaces to bereferred to, using two reference lists (list L0 and list L1). When afirst reference space in list L0 is L0R0, a second reference space inlist L0 is L0R1, a first reference space in list L1 is L1R0, and asecond reference space in list L1 is L1R1, three-dimensional dataencoding device 1300 appends, to the bitstream, RT information RT_L0R0of L0R0, RT information RT_L0R1 of L0R1, RT information RT_L1R0 of L1R0,and RT information RT_L1R1 of L1R1. For example, three-dimensional dataencoding device 1300 appends these pieces of RT information to a headerand the like of the bitstream.

Three-dimensional data encoding device 1300 determines whether to applyrotation and translation per reference space, when encoding withreference to reference spaces associated with different times. In thiscase, three-dimensional data encoding device 1300 may append, to headerinformation and the like of the bitstream, information (RT flag, etc.)indicating whether rotation and translation are applied per referencespace. For example, three-dimensional data encoding device 1300calculates the RT information and an Iterative Closest Point (ICP) errorvalue, using an ICP algorithm per reference space to be referred to fromthe encoding target space. Three-dimensional data encoding device 1300determines that rotation and translation do not need to be performed andsets the RT flag to OFF, when the ICP error value is lower than or equalto a predetermined fixed value. In contrast, three-dimensional dataencoding device 1300 sets the RT flag to ON and appends the RTinformation to the bitstream, when the ICP error value exceeds the abovefixed value.

FIG. 46 is a diagram showing an example syntax to be appended to aheader of the RT information and the RT flag. Note that a bit countassigned to each syntax may be decided based on a range of this syntax.For example, when eight reference spaces are included in reference listL0, 3 bits may be assigned to MaxRefSpc_l0. The bit count to be assignedmay be variable in accordance with a value each syntax can be, and mayalso be fixed regardless of the value each syntax can be. When the bitcount to be assigned is fixed, three-dimensional data encoding device1300 may append this fixed bit count to other header information.

MaxRefSpc_l0 shown in FIG. 46 indicates a number of reference spacesincluded in reference list L0. RT_flag_l0[i] is an RT flag of referencespace i in reference list L0. When RT_flag_l0[i] is 1, rotation andtranslation are applied to reference space i. When RT_flag_l0[i] is 0,rotation and translation are not applied to reference space i.

R_l0[i] and T_l0[i] are RT information of reference space i in referencelist L0. R_l0[i] is rotation information of reference space i inreference list L0. The rotation information indicates contents of theapplied rotation process, and is, for example, a rotation matrix or aquaternion. T_l0[i] is translation information of reference space i inreference list L0. The translation information indicates contents of theapplied translation process, and is, for example, a translation vector.

MaxRefSpc_l1 indicates a number of reference spaces included inreference list L1. RT_flag_l1[i] is an RT flag of reference space i inreference list L1. When RT_flag_l1[i] is 1, rotation and translation areapplied to reference space i. When RT_flag_l1[i] is 0, rotation andtranslation are not applied to reference space i.

R_l1[i] and T_l1[i] are RT information of reference space i in referencelist L1. R_l1[i] is rotation information of reference space i inreference list L1. The rotation information indicates contents of theapplied rotation process, and is, for example, a rotation matrix or aquaternion. T_l1[i] is translation information of reference space i inreference list L1. The translation information indicates contents of theapplied translation process, and is, for example, a translation vector.

Inter predictor 1311 generates the predicted volume of the encodingtarget volume using information on an encoded reference space stored inreference space memory 1310. As stated above, before generating thepredicted volume of the encoding target volume, inter predictor 1311calculates RT information at an encoding target space and a referencespace using an ICP algorithm, in order to approach an overall positionalrelationship between the encoding target space and the reference space.Inter predictor 1311 then obtains reference space B by applying arotation and translation process to the reference space using thecalculated RT information. Subsequently, inter predictor 1311 generatesthe predicted volume of the encoding target volume in the encodingtarget space using information in reference space B. Three-dimensionaldata encoding device 1300 appends, to header information and the like ofthe encoding target space, the RT information used to obtain referencespace B.

In this manner, inter predictor 1311 is capable of improving precisionof the predicted volume by generating the predicted volume using theinformation of the reference space, after approaching the overallpositional relationship between the encoding target space and thereference space, by applying a rotation and translation process to thereference space. It is possible to reduce the code amount since it ispossible to limit the prediction residual. Note that an example has beendescribed in which ICP is performed using the encoding target space andthe reference space, but is not necessarily limited thereto. Forexample, inter predictor 1311 may calculate the RT information byperforming ICP using at least one of (i) an encoding target space inwhich a voxel or point cloud count is pruned, or (ii) a reference spacein which a voxel or point cloud count is pruned, in order to reduce theprocessing amount.

When the ICP error value obtained as a result of the ICP is smaller thana predetermined first threshold, i.e., when for example the positionalrelationship between the encoding target space and the reference spaceis similar, inter predictor 1311 determines that a rotation andtranslation process is not necessary, and the rotation and translationprocess does not need to be performed. In this case, three-dimensionaldata encoding device 1300 may control the overhead by not appending theRT information to the bitstream.

When the ICP error value is greater than a predetermined secondthreshold, inter predictor 1311 determines that a shape change betweenthe spaces is large, and intra prediction may be applied on all volumesof the encoding target space. Hereinafter, spaces to which intraprediction is applied will be referred to as intra spaces. The secondthreshold is greater than the above first threshold. The presentembodiment is not limited to ICP, and any type of method may be used aslong as the method calculates the RT information using two voxel sets ortwo point cloud sets.

When attribute information, e.g. shape or color information, is includedin the three-dimensional data, inter predictor 1311 searches, forexample, a volume whose attribute information, e.g. shape or colorinformation, is the most similar to the encoding target volume in thereference space, as the predicted volume of the encoding target volumein the encoding target space. This reference space is, for example, areference space on which the above rotation and translation process hasbeen performed. Inter predictor 1311 generates the predicted volumeusing the volume (reference volume) obtained through the search. FIG. 47is a diagram for describing a generating operation of the predictedvolume. When encoding the encoding target volume (volume idx=0) shown inFIG. 47 using inter prediction, inter predictor 1311 searches a volumewith a smallest prediction residual, which is the difference between theencoding target volume and the reference volume, while sequentiallyscanning the reference volume in the reference space. Inter predictor1311 selects the volume with the smallest prediction residual as thepredicted volume. The prediction residuals of the encoding target volumeand the predicted volume are encoded through the processes performed bytransformer 1303 and subsequent processors. The prediction residual hereis a difference between the attribute information of the encoding targetvolume and the attribute information of the predicted volume.Three-dimensional data encoding device 1300 appends, to the header andthe like of the bitstream, volume idx of the reference volume in thereference space, as the predicted volume.

In the example shown in FIG. 47 , the reference volume with volume idx=4of reference space L0R0 is selected as the predicted volume of theencoding target volume. The prediction residuals of the encoding targetvolume and the reference volume, and reference volume idx=4 are thenencoded and appended to the bitstream.

Note that an example has been described in which the predicted volume ofthe attribute information is generated, but the same process may beapplied to the predicted volume of the position information.

Prediction controller 1312 controls whether to encode the encodingtarget volume using intra prediction or inter prediction. A modeincluding intra prediction and inter prediction is referred to here as aprediction mode. For example, prediction controller 1312 calculates theprediction residual when the encoding target volume is predicted usingintra prediction and the prediction residual when the encoding targetvolume is predicted using inter prediction as evaluation values, andselects the prediction mode whose evaluation value is smaller. Note thatprediction controller 1312 may calculate an actual code amount byapplying orthogonal transformation, quantization, and entropy encodingto the prediction residual of the intra prediction and the predictionresidual of the inter prediction, and select a prediction mode using thecalculated code amount as the evaluation value. Overhead information(reference volume idx information, etc.) aside from the predictionresidual may be added to the evaluation value. Prediction controller1312 may continuously select intra prediction when it has been decidedin advance to encode the encoding target space using intra space.

Entropy encoder 1313 generates an encoded signal (encoded bitstream) byvariable-length encoding the quantized coefficient, which is an inputfrom quantizer 1304. To be specific, entropy encoder 1313, for example,binarizes the quantized coefficient and arithmetically encodes theobtained binary signal.

A three-dimensional data decoding device that decodes the encoded signalgenerated by three-dimensional data encoding device 1300 will bedescribed next. FIG. 48 is a block diagram of three-dimensional datadecoding device 1400 according to the present embodiment. Thisthree-dimensional data decoding device 1400 includes entropy decoder1401, inverse quantizer 1402, inverse transformer 1403, adder 1404,reference volume memory 1405, intra predictor 1406, reference spacememory 1407, inter predictor 1408, and prediction controller 1409.

Entropy decoder 1401 variable-length decodes the encoded signal (encodedbitstream). For example, entropy decoder 1401 generates a binary signalby arithmetically decoding the encoded signal, and generates a quantizedcoefficient using the generated binary signal.

Inverse quantizer 1402 generates an inverse quantized coefficient byinverse quantizing the quantized coefficient inputted from entropydecoder 1401, using a quantization parameter appended to the bitstreamand the like.

Inverse transformer 1403 generates a prediction residual by inversetransforming the inverse quantized coefficient inputted from inversequantizer 1402. For example, inverse transformer 1403 generates theprediction residual by inverse orthogonally transforming the inversequantized coefficient, based on information appended to the bitstream.

Adder 1404 adds, to generate a reconstructed volume, (i) the predictionresidual generated by inverse transformer 1403 to (ii) a predictedvolume generated through intra prediction or intra prediction. Thisreconstructed volume is outputted as decoded three-dimensional data andis stored in reference volume memory 1405 or reference space memory1407.

Intra predictor 1406 generates a predicted volume through intraprediction using a reference volume in reference volume memory 1405 andinformation appended to the bitstream. To be specific, intra predictor1406 obtains neighboring volume information (e.g. volume idx) appendedto the bitstream and prediction mode information, and generates thepredicted volume through a mode indicated by the prediction modeinformation, using a neighboring volume indicated in the neighboringvolume information. Note that the specifics of these processes are thesame as the above-mentioned processes performed by intra predictor 1309,except for which information appended to the bitstream is used.

Inter predictor 1408 generates a predicted volume through interprediction using a reference space in reference space memory 1407 andinformation appended to the bitstream. To be specific, inter predictor1408 applies a rotation and translation process to the reference spaceusing the RT information per reference space appended to the bitstream,and generates the predicted volume using the rotated and translatedreference space. Note that when an RT flag is present in the bitstreamper reference space, inter predictor 1408 applies a rotation andtranslation process to the reference space in accordance with the RTflag. Note that the specifics of these processes are the same as theabove-mentioned processes performed by inter predictor 1311, except forwhich information appended to the bitstream is used.

Prediction controller 1409 controls whether to decode a decoding targetvolume using intra prediction or inter prediction. For example,prediction controller 1409 selects intra prediction or inter predictionin accordance with information that is appended to the bitstream andindicates the prediction mode to be used. Note that predictioncontroller 1409 may continuously select intra prediction when it hasbeen decided in advance to decode the decoding target space using intraspace.

Hereinafter, variations of the present embodiment will be described. Inthe present embodiment, an example has been described in which rotationand translation is applied in units of spaces, but rotation andtranslation may also be applied in smaller units. For example,three-dimensional data encoding device 1300 may divide a space intosubspaces, and apply rotation and translation in units of subspaces. Inthis case, three-dimensional data encoding device 1300 generates RTinformation per subspace, and appends the generated RT information to aheader and the like of the bitstream. Three-dimensional data encodingdevice 1300 may apply rotation and translation in units of volumes,which is an encoding unit. In this case, three-dimensional data encodingdevice 1300 generates RT information in units of encoded volumes, andappends the generated RT information to a header and the like of thebitstream. The above may also be combined. In other words,three-dimensional data encoding device 1300 may apply rotation andtranslation in large units and subsequently apply rotation andtranslation in small units. For example, three-dimensional data encodingdevice 1300 may apply rotation and translation in units of spaces, andmay also apply different rotations and translations to each of aplurality of volumes included in the obtained spaces.

In the present embodiment, an example has been described in whichrotation and translation is applied to the reference space, but is notnecessarily limited thereto. For example, three-dimensional dataencoding device 1300 may apply a scaling process and change a size ofthe three-dimensional data. Three-dimensional data encoding device 1300may also apply one or two of the rotation, translation, and scaling.When applying the processes in multiple stages and different units asstated above, a type of the processes applied in each unit may differ.For example, rotation and translation may be applied in units of spaces,and translation may be applied in units of volumes.

Note that these variations are also applicable to three-dimensional datadecoding device 1400.

As stated above, three-dimensional data encoding device 1300 accordingto the present embodiment performs the following processes. FIG. 48 is aflowchart of the inter prediction process performed by three-dimensionaldata encoding device 1300.

Three-dimensional data encoding device 1300 generates predicted positioninformation (e.g. predicted volume) using position information onthree-dimensional points included in three-dimensional reference data(e.g. reference space) associated with a time different from a timeassociated with current three-dimensional data (e.g. encoding targetspace) (S1301). To be specific, three-dimensional data encoding device1300 generates the predicted position information by applying a rotationand translation process to the position information on thethree-dimensional points included in the three-dimensional referencedata.

Note that three-dimensional data encoding device 1300 may perform arotation and translation process using a first unit (e.g. spaces), andmay perform the generating of the predicted position information using asecond unit (e.g. volumes) that is smaller than the first unit. Forexample, three-dimensional data encoding device 1300 searches a volumeamong a plurality of volumes included in the rotated and translatedreference space, whose position information differs the least from theposition information of the encoding target volume included in theencoding target space. Note that three-dimensional data encoding device1300 may perform the rotation and translation process, and thegenerating of the predicted position information in the same unit.

Three-dimensional data encoding device 1300 may generate the predictedposition information by applying (i) a first rotation and translationprocess to the position information on the three-dimensional pointsincluded in the three-dimensional reference data, and (ii) a secondrotation and translation process to the position information on thethree-dimensional points obtained through the first rotation andtranslation process, the first rotation and translation process using afirst unit (e.g. spaces) and the second rotation and translation processusing a second unit (e.g. volumes) that is smaller than the first unit.

For example, as illustrated in FIG. 41 , the position information on thethree-dimensional points and the predicted position information isrepresented using an octree structure. For example, the positioninformation on the three-dimensional points and the predicted positioninformation is expressed in a scan order that prioritizes a breadth overa depth in the octree structure. For example, the position informationon the three-dimensional points and the predicted position informationis expressed in a scan order that prioritizes a depth over a breadth inthe octree structure.

As illustrated in FIG. 46 , three-dimensional data encoding device 1300encodes an RT flag that indicates whether to apply the rotation andtranslation process to the position information on the three-dimensionalpoints included in the three-dimensional reference data. In other words,three-dimensional data encoding device 1300 generates the encoded signal(encoded bitstream) including the RT flag. Three-dimensional dataencoding device 1300 encodes RT information that indicates contents ofthe rotation and translation process. In other words, three-dimensionaldata encoding device 1300 generates the encoded signal (encodedbitstream) including the RT information. Note that three-dimensionaldata encoding device 1300 may encode the RT information when the RT flagindicates to apply the rotation and translation process, and does notneed to encode the RT information when the RT flag indicates not toapply the rotation and translation process.

The three-dimensional data includes, for example, the positioninformation on the three-dimensional points and the attributeinformation (color information, etc.) of each three-dimensional point.Three-dimensional data encoding device 1300 generates predictedattribute information using the attribute information of thethree-dimensional points included in the three-dimensional referencedata (S1302).

Three-dimensional data encoding device 1300 next encodes the positioninformation on the three-dimensional points included in the currentthree-dimensional data, using the predicted position information. Forexample, as illustrated in FIG. 38 , three-dimensional data encodingdevice 1300 calculates differential position information, thedifferential position information being a difference between thepredicted position information and the position information on thethree-dimensional points included in the current three-dimensional data(S1303).

Three-dimensional data encoding device 1300 encodes the attributeinformation of the three-dimensional points included in the currentthree-dimensional data, using the predicted attribute information. Forexample, three-dimensional data encoding device 1300 calculatesdifferential attribute information, the differential attributeinformation being a difference between the predicted attributeinformation and the attribute information on the three-dimensionalpoints included in the current three-dimensional data (S1304).Three-dimensional data encoding device 1300 next performs transformationand quantization on the calculated differential attribute information(S1305).

Lastly, three-dimensional data encoding device 1300 encodes (e.g.entropy encodes) the differential position information and the quantizeddifferential attribute information (S1036). In other words,three-dimensional data encoding device 1300 generates the encoded signal(encoded bitstream) including the differential position information andthe differential attribute information.

Note that when the attribute information is not included in thethree-dimensional data, three-dimensional data encoding device 1300 doesnot need to perform steps S1302, S1304, and S1305. Three-dimensionaldata encoding device 1300 may also perform only one of the encoding ofthe position information on the three-dimensional points and theencoding of the attribute information of the three-dimensional points.

An order of the processes shown in FIG. 49 is merely an example and isnot limited thereto. For example, since the processes with respect tothe position information (S1301 and S1303) and the processes withrespect to the attribute information (S1302, S1304, and S1305) areseparate from one another, they may be performed in an order of choice,and a portion thereof may also be performed in parallel.

With the above, three-dimensional data encoding device 1300 according tothe present embodiment generates predicted position information usingposition information on three-dimensional points included inthree-dimensional reference data associated with a time different from atime associated with current three-dimensional data; and encodesdifferential position information, which is a difference between thepredicted position information and the position information on thethree-dimensional points included in the current three-dimensional data.This makes it possible to improve encoding efficiency since it ispossible to reduce the amount of data of the encoded signal.

Three-dimensional data encoding device 1300 according to the presentembodiment generates predicted attribute information using attributeinformation on three-dimensional points included in three-dimensionalreference data; and encodes differential attribute information, which isa difference between the predicted attribute information and theattribute information on the three-dimensional points included in thecurrent three-dimensional data. This makes it possible to improveencoding efficiency since it is possible to reduce the amount of data ofthe encoded signal.

For example, three-dimensional data encoding device 1300 includes aprocessor and memory. The processor uses the memory to perform the aboveprocesses.

FIG. 48 is a flowchart of the inter prediction process performed bythree-dimensional data decoding device 1400.

Three-dimensional data decoding device 1400 decodes (e.g. entropydecodes) the differential position information and the differentialattribute information from the encoded signal (encoded bitstream)(S1401).

Three-dimensional data decoding device 1400 decodes, from the encodedsignal, an RT flag that indicates whether to apply the rotation andtranslation process to the position information on the three-dimensionalpoints included in the three-dimensional reference data.Three-dimensional data decoding device 1400 encodes RT information thatindicates contents of the rotation and translation process. Note thatthree-dimensional data decoding device 1400 may decode the RTinformation when the RT flag indicates to apply the rotation andtranslation process, and does not need to decode the RT information whenthe RT flag indicates not to apply the rotation and translation process.

Three-dimensional data decoding device 1400 next performs inversetransformation and inverse quantization on the decoded differentialattribute information (S1402).

Three-dimensional data decoding device 1400 next generates predictedposition information (e.g. predicted volume) using the positioninformation on the three-dimensional points included in thethree-dimensional reference data (e.g. reference space) associated witha time different from a time associated with the currentthree-dimensional data (e.g. decoding target space) (S1403). To bespecific, three-dimensional data decoding device 1400 generates thepredicted position information by applying a rotation and translationprocess to the position information on the three-dimensional pointsincluded in the three-dimensional reference data.

More specifically, when the RT flag indicates to apply the rotation andtranslation process, three-dimensional data decoding device 1400 appliesthe rotation and translation process on the position information on thethree-dimensional points included in the three-dimensional referencedata indicated in the RT information. In contrast, when the RT flagindicates not to apply the rotation and translation process,three-dimensional data decoding device 1400 does not apply the rotationand translation process on the position information on thethree-dimensional points included in the three-dimensional referencedata.

Note that three-dimensional data decoding device 1400 may perform therotation and translation process using a first unit (e.g. spaces), andmay perform the generating of the predicted position information using asecond unit (e.g. volumes) that is smaller than the first unit. Notethat three-dimensional data decoding device 1400 may perform therotation and translation process, and the generating of the predictedposition information in the same unit.

Three-dimensional data decoding device 1400 may generate the predictedposition information by applying (i) a first rotation and translationprocess to the position information on the three-dimensional pointsincluded in the three-dimensional reference data, and (ii) a secondrotation and translation process to the position information on thethree-dimensional points obtained through the first rotation andtranslation process, the first rotation and translation process using afirst unit (e.g. spaces) and the second rotation and translation processusing a second unit (e.g. volumes) that is smaller than the first unit.

For example, as illustrated in FIG. 41 , the position information on thethree-dimensional points and the predicted position information isrepresented using an octree structure. For example, the positioninformation on the three-dimensional points and the predicted positioninformation is expressed in a scan order that prioritizes a breadth overa depth in the octree structure. For example, the position informationon the three-dimensional points and the predicted position informationis expressed in a scan order that prioritizes a depth over a breadth inthe octree structure.

Three-dimensional data decoding device 1400 generates predictedattribute information using the attribute information of thethree-dimensional points included in the three-dimensional referencedata (S1404).

Three-dimensional data decoding device 1400 next restores the positioninformation on the three-dimensional points included in the currentthree-dimensional data, by decoding encoded position informationincluded in an encoded signal, using the predicted position information.The encoded position information here is the differential positioninformation. Three-dimensional data decoding device 1400 restores theposition information on the three-dimensional points included in thecurrent three-dimensional data, by adding the differential positioninformation to the predicted position information (S1405).

Three-dimensional data decoding device 1400 restores the attributeinformation of the three-dimensional points included in the currentthree-dimensional data, by decoding encoded attribute informationincluded in an encoded signal, using the predicted attributeinformation. The encoded attribute information here is the differentialposition information. Three-dimensional data decoding device 1400restores the attribute information on the three-dimensional pointsincluded in the current three-dimensional data, by adding thedifferential attribute information to the predicted attributeinformation (S1406).

Note that when the attribute information is not included in thethree-dimensional data, three-dimensional data decoding device 1400 doesnot need to perform steps S1402, S1404, and S1406. Three-dimensionaldata decoding device 1400 may also perform only one of the decoding ofthe position information on the three-dimensional points and thedecoding of the attribute information of the three-dimensional points.

An order of the processes shown in FIG. 50 is merely an example and isnot limited thereto. For example, since the processes with respect tothe position information (S1403 and S1405) and the processes withrespect to the attribute information (S1402, S1404, and S1406) areseparate from one another, they may be performed in an order of choice,and a portion thereof may also be performed in parallel.

Embodiment 8

Information of a three-dimensional point cloud includes geometryinformation (geometry) and attribute information (attribute). Geometryinformation includes coordinates (x-coordinate, y-coordinate,z-coordinate) with respect to a certain point. When geometry informationis encoded, a method of representing the position of each ofthree-dimensional points in octree representation and encoding theoctree information to reduce a code amount is used instead of directlyencoding the coordinates of the three-dimensional point. On the otherhand, attribute information includes information indicating, forexample, color information (RGB, YUV, etc.) of each three-dimensionalpoint, a reflectance, and a normal vector. For example, athree-dimensional data encoding device is capable of encoding attributeinformation using an encoding method different from a method used toencode geometry information.

In the present embodiment, a method of encoding attribute information isexplained. It is to be noted that, in the present embodiment, the methodis explained based on an example case using integer values as values ofattribute information. For example, when each of RGB or YUV colorcomponents is of an 8-bit accuracy, the color component is an integervalue in a range from 0 to 255. When a reflectance value is of 10-bitaccuracy, the reflectance value is an integer in a range from 0 to 1023.It is to be noted that, when the bit accuracy of attribute informationis a decimal accuracy, the three-dimensional data encoding device maymultiply the value by a scale value to round it to an integer value sothat the value of the attribute information becomes an integer value. Itis to be noted that the three-dimensional data encoding device may addthe scale value to, for example, a header of a bitstream.

As a method of encoding attribute information of a three-dimensionalpoint, it is conceivable to calculate a predicted value of the attributeinformation of the three-dimensional point and encode a difference(prediction residual) between the original value of the attributeinformation and the predicted value. For example, when the value ofattribute information at three-dimensional point p is Ap and a predictedvalue is Pp, the three-dimensional data encoding device encodesdifferential absolute value Diffp=|Ap·Pp|. In this case, whenhighly-accurate predicted value Pp can be generated, differentialabsolute value Diffp is small. Thus, for example, it is possible toreduce the code amount by entropy encoding differential absolute valueDiffp using a coding table that reduces an occurrence bit count morewhen differential absolute value Diffp is smaller.

As a method of generating a prediction value of attribute information,it is conceivable to use attribute information of a referencethree-dimensional point that is another three-dimensional point whichneighbors a current three-dimensional point to be encoded. Here, areference three-dimensional point is a three-dimensional point in arange of a predetermined distance from the current three-dimensionalpoint. For example, when there are current three-dimensional pointp=(x1, y1, z1) and three-dimensional point q=(x2, y2, z2), thethree-dimensional data encoding device calculates Euclidean distance d(p, q) between three-dimensional point p and three-dimensional point qrepresented by (Equation A1).

[Math. 1]

d(p,q)=√{square root over ((x1−y1)²+(x2−y2)²+(x3−y3)²)}  (Equation A1)

The three-dimensional data encoding device determines that the positionof three-dimensional point q is closer to the position of currentthree-dimensional point p when Euclidean distance d (p, q) is smallerthan predetermined threshold value THd, and determines to use the valueof the attribute information of three-dimensional point q to generate apredicted value of the attribute information of currentthree-dimensional point p. It is to be noted that the method ofcalculating the distance may be another method, and a Mahalanobisdistance or the like may be used. In addition, the three-dimensionaldata encoding device may determine not to use, in prediction processing,any three-dimensional point outside the predetermined range of distancefrom the current three-dimensional point. For example, whenthree-dimensional point r is present, and distance d (p, r) betweencurrent three-dimensional point p and three-dimensional point r islarger than or equal to threshold value THd, the three-dimensional dataencoding device may determine not to use three-dimensional point r forprediction. It is to be noted that the three-dimensional data encodingdevice may add the information indicating threshold value THd to, forexample, a header of a bitstream.

FIG. 51 is a diagram illustrating an example of three-dimensionalpoints. In this example, distance d (p, q) between currentthree-dimensional point p and three-dimensional point q is smaller thanthreshold value THd. Thus, the three-dimensional data encoding devicedetermines that three-dimensional point q is a referencethree-dimensional point of current three-dimensional point p, anddetermines to use the value of attribute information Aq ofthree-dimensional point q to generate predicted value Pp of attributeinformation Ap of current three-dimensional point p.

In contrast, distance d (p, r) between current three-dimensional point pand three-dimensional point r is larger than or equal to threshold valueTHd. Thus, the three-dimensional data encoding device determines thatthree-dimensional point r is not any reference three-dimensional pointof current three-dimensional point p, and determines not to use thevalue of attribute information Ar of three-dimensional point r togenerate predicted value Pp of attribute information Ap of currentthree-dimensional point p.

In addition, when encoding the attribute information of the currentthree-dimensional point using a predicted value, the three-dimensionaldata encoding device uses a three-dimensional point whose attributeinformation has already been encoded and decoded, as a referencethree-dimensional point. Likewise, when decoding the attributeinformation of a current three-dimensional point to be decoded, thethree-dimensional data decoding device uses a three-dimensional pointwhose attribute information has already been decoded, as a referencethree-dimensional point. In this way, it is possible to generate thesame predicted value at the time of encoding and decoding. Thus, abitstream of the three-dimensional point generated by the encoding canbe decoded correctly at the decoding side.

Furthermore, when encoding attribute information of each ofthree-dimensional points, it is conceivable to classify thethree-dimensional point into one of a plurality of layers using geometryinformation of the three-dimensional point and then encode the attributeinformation. Here, each of the layers classified is referred to as aLevel of Detail (LoD). A method of generating LoDs is explained withreference to FIG. 52 .

First, the three-dimensional data encoding device selects initial pointa0 and assigns initial point a0 to LoD0. Next, the three-dimensionaldata encoding device extracts point a1 distant from point a0 more thanthreshold value Thres_LoD[0] of LoD0 and assigns point a1 to LoD0. Next,the three-dimensional data encoding device extracts point a2 distantfrom point a1 more than threshold value Thres_LoD[0] of LoD0 and assignspoint a2 to LoD0. In this way, the three-dimensional data encodingdevice configures LoD0 in such a manner that the distance between thepoints in LoD0 is larger than threshold value Thres_LoD[0].

Next, the three-dimensional data encoding device selects point b0 whichhas not yet been assigned to any LoD and assigns point b0 to LoD1. Next,the three-dimensional data encoding device extracts point b1 which isdistant from point b0 more than threshold value Thres_LoD[1] of LoD1 andwhich has not yet been assigned to any LoD, and assigns point b1 toLoD1. Next, the three-dimensional data encoding device extracts point b2which is distant from point b1 more than threshold value Thres_LoD[1] ofLoD1 and which has not yet been assigned to any LoD, and assigns pointb2 to LoD1. In this way, the three-dimensional data encoding deviceconfigures LoD1 in such a manner that the distance between the points inLoD1 is larger than threshold value Thres_LoD[1].

Next, the three-dimensional data encoding device selects point c0 whichhas not yet been assigned to any LoD and assigns point c0 to LoD2. Next,the three-dimensional data encoding device extracts point c1 which isdistant from point c0 more than threshold value Thres_LoD[2] of LoD2 andwhich has not yet been assigned to any LoD, and assigns point c1 toLoD2. Next, the three-dimensional data encoding device extracts point c2which is distant from point c1 more than threshold value Thres_LoD[2] ofLoD2 and which has not yet been assigned to any LoD, and assigns pointc2 to LoD2. In this way, the three-dimensional data encoding deviceconfigures LoD2 in such a manner that the distance between the points inLoD2 is larger than threshold value Thres_LoD[2]. For example, asillustrated in FIG. 53 , threshold values Thres_LoD[0], Thres_LoD[1],and Thres_LoD[2] of respective LoDs are set.

In addition, the three-dimensional data encoding device may add theinformation indicating the threshold value of each LoD to, for example,a header of a bitstream. For example, in the case of the exampleillustrated in FIG. 53 , the three-dimensional data encoding device mayadd threshold values Thres_LoD[0], Thres_LoD[1], and Thres_LoD[2] ofrespective LoDs to a header.

Alternatively, the three-dimensional data encoding device may assign allthree-dimensional points which have not yet been assigned to any LoD inthe lowermost-layer LoD. In this case, the three-dimensional dataencoding device is capable of reducing the code amount of the header bynot assigning the threshold value of the lowermost-layer LoD to theheader. For example, in the case of the example illustrated in FIG. 53 ,the three-dimensional data encoding device assigns threshold valuesThres_LoD[0] and Thres_LoD[1] to the header, and does not assignThres_LoD[2] to the header. In this case, the three-dimensional dataencoding device may estimate value 0 of Thres_LoD[2]. In addition, thethree-dimensional data encoding device may add the number of LoDs to aheader. In this way, the three-dimensional data encoding device iscapable of determining the lowermost-layer LoD using the number of LoDs.

In addition, setting threshold values for the respective layers LoDs insuch a manner that a larger threshold value is set to a higher layer, asillustrated in FIG. 53 , makes a higher layer (layer closer to LoD0) tohave a sparse point cloud (sparse) in which three-dimensional points aremore distant and makes a lower layer to have a dense point cloud (dense)in which three-dimensional points are closer. It is to be noted that, inan example illustrated in FIG. 53 , LoD0 is the uppermost layer.

In addition, the method of selecting an initial three-dimensional pointat the time of setting each LoD may depend on an encoding order at thetime of geometry information encoding. For example, thethree-dimensional data encoding device configures LoD0 by selecting thethree-dimensional point encoded first at the time of the geometryinformation encoding as initial point a0 of LoD0, and selecting point a1and point a2 from initial point a0 as the origin. The three-dimensionaldata encoding device then may select the three-dimensional point whosegeometry information has been encoded at the earliest time amongthree-dimensional points which do not belong to LoD0, as initial pointb0 of LoD1. In other words, the three-dimensional data encoding devicemay select the three-dimensional point whose geometry information hasbeen encoded at the earliest time among three-dimensional points whichdo not belong to layers (LoD0 to LoDn−1) above LoDn, as initial point n0of LoDn. In this way, the three-dimensional data encoding device iscapable of configuring the same LoD as in encoding by using, indecoding, the initial point selecting method similar to the one used inthe encoding, which enables appropriate decoding of a bitstream. Morespecifically, the three-dimensional data encoding device selects thethree-dimensional point whose geometry information has been decoded atthe earliest time among three-dimensional points which do not belong tolayers above LoDn, as initial point n0 of LoDn.

Hereinafter, a description is given of a method of generating thepredicted value of the attribute information of each three-dimensionalpoint using information of LoDs. For example, when encodingthree-dimensional points starting with the three-dimensional pointsincluded in LoD0, the three-dimensional data encoding device generatescurrent three-dimensional points which are included in LoD1 usingencoded and decoded (hereinafter also simply referred to as “encoded”)attribute information included in LoD0 and LoD1. In this way, thethree-dimensional data encoding device generates a predicted value ofattribute information of each three-dimensional point included in LoDnusing encoded attribute information included in LoDn′ (n′≤n). In otherwords, the three-dimensional data encoding device does not use attributeinformation of each of three-dimensional points included in any layerbelow LoDn to calculate a predicted value of attribute information ofeach of the three-dimensional points included in LoDn.

For example, the three-dimensional data encoding device calculates anaverage of attribute information of N or less three dimensional pointsamong encoded three-dimensional points surrounding a currentthree-dimensional point to be encoded, to generate a predicted value ofattribute information of the current three-dimensional point. Inaddition, the three-dimensional data encoding device may add value N to,for example, a header of a bitstream. It is to be noted that thethree-dimensional data encoding device may change value N for eachthree-dimensional point, and may add value N for each three-dimensionalpoint. This enables selection of appropriate N for eachthree-dimensional point, which makes it possible to increase theaccuracy of the predicted value. Accordingly, it is possible to reducethe prediction residual. Alternatively, the three-dimensional dataencoding device may add value N to a header of a bitstream, and may fixthe value indicating N in the bitstream. This eliminates the need toencode or decode value N for each three-dimensional point, which makesit possible to reduce the processing amount. In addition, thethree-dimensional data encoding device may encode the values of Nseparately for each LoD. In this way, it is possible to increase thecoding efficiency by selecting appropriate N for each LoD.

Alternatively, the three-dimensional data encoding device may calculatea predicted value of attribute information of three-dimensional pointbased on weighted average values of attribute information of encoded Nneighbor three-dimensional points. For example, the three-dimensionaldata encoding device calculates weights using distance informationbetween a current three-dimensional point and each of N neighborthree-dimensional points.

When encoding value N for each LoD, for example, the three-dimensionaldata encoding device sets larger value N to a higher layer LoD, and setssmaller value N to a lower layer LoD. The distance betweenthree-dimensional points belonging to a higher layer LoD is large, thereis a possibility that it is possible to increase the prediction accuracyby setting large value N, selecting a plurality of neighborthree-dimensional points, and averaging the values. Furthermore, thedistance between three-dimensional points belonging to a lower layer LoDis small, it is possible to perform efficient prediction while reducingthe processing amount of averaging by setting smaller value N.

FIG. 54 is a diagram illustrating an example of attribute information tobe used for predicted values. As described above, the predicted value ofpoint P included in LoDN is generated using encoded neighbor point P′included in LoDN′ (N′≤N). Here, neighbor point P′ is selected based onthe distance from point P. For example, the predicted value of attributeinformation of point b2 illustrated in FIG. 54 is generated usingattribute information of each of points a0, a1, b0, and b1.

Neighbor points to be selected vary depending on the values of Ndescribed above. For example, in the case of N=5, a0, a1, a2, b0, and b1are selected as neighbor points. In the case of N=4, a0, a1, a2, and b1are selected based on distance information.

The predicted value is calculated by distance-dependent weightedaveraging. For example, in the example illustrated in FIG. 54 ,predicted value a2p of point a2 is calculated by weighted averaging ofattribute information of each of point a0 and a1, as represented by(Equation A2) and (Equation A3). It is to be noted that A_(i) is anattribute information value of ai.

$\begin{matrix}\lbrack {{Math}.2} \rbrack &  \\{{a2p} = {\sum\limits_{i = 0}^{1}{w_{i} \times A_{i}}}} & ( {{Equation}{A2}} )\end{matrix}$ $\begin{matrix}{w_{i} = \frac{\frac{1}{d( {{a2},{ai}} )}}{{\sum}_{j = 0}^{1}\frac{1}{d( {{a2},{aj}} )}}} & ( {{Equation}{A3}} )\end{matrix}$

In addition, predicted value b2p of point b2 is calculated by weightedaveraging of attribute information of each of point a0, a1, a2, b0, andb1, as represented by (Equation A4) and (Equation A6). It is to be notedthat B_(i) is an attribute information value of bi.

$\begin{matrix}\lbrack {{Math}.3} \rbrack &  \\{{b2p} = {{{\sum}_{i = 0}^{2}{wa}_{i} \times A_{i}} + {{\sum}_{i = 0}^{1}{wb}_{i} \times B_{i}}}} & \text{(Equation  A4)}\end{matrix}$ $\begin{matrix}{{wa}_{i} = \frac{\frac{1}{d( {{b2},{ai}} )}}{{{\sum}_{j = 0}^{2}\frac{1}{d( {{b2},{aj}} )}} + {{\sum}_{j = 0}^{1}\frac{1}{d( {{b2},{bj}} )}}}} & \text{(Equation  A5)}\end{matrix}$ $\begin{matrix}{{wb}_{i} = \frac{\frac{1}{d( {{b2},{bi}} )}}{{{\sum}_{j = 0}^{2}\frac{1}{d( {{b2},{aj}} )}} + {{\sum}_{j = 0}^{1}\frac{1}{d( {{b2},{bj}} )}}}} & \text{(Equation  A6)}\end{matrix}$

In addition, the three-dimensional data encoding device may calculate adifference value (prediction residual) generated from the value ofattribute information of a three-dimensional point and neighbor points,and may quantize the calculated prediction residual. For example, thethree-dimensional data encoding device performs quantization by dividingthe prediction residual by a quantization scale (also referred to as aquantization step). In this case, an error (quantization error) whichmay be generated by quantization reduces as the quantization scale issmaller. In the other case where the quantization scale is larger, theresulting quantization error is larger.

It is to be noted that the three-dimensional data encoding device maychange the quantization scale to be used for each LoD. For example, thethree-dimensional data encoding device reduces the quantization scalemore for a higher layer, and increases the quantization scale more for alower layer. The value of attribute information of a three-dimensionalpoint belonging to a higher layer may be used as a predicted value ofattribute information of a three-dimensional point belonging to a lowerlayer. Thus, it is possible to increase the coding efficiency byreducing the quantization scale for the higher layer to reduce thequantization error that can be generated in the higher layer and toincrease the prediction accuracy of the predicted value. It is to benoted that the three-dimensional data encoding device may add thequantization scale to be used for each LoD to, for example, a header. Inthis way, the three-dimensional data encoding device can decode thequantization scale correctly, thereby appropriately decoding thebitstream.

In addition, the three-dimensional data encoding device may convert asigned integer value (signed quantized value) which is a quantizedprediction residual into an unsigned integer value (unsigned quantizedvalue). This eliminates the need to consider occurrence of a negativeinteger when entropy encoding the prediction residual. It is to be notedthat the three-dimensional data encoding device does not always need toconvert a signed integer value into an unsigned integer value, and, forexample, that the three-dimensional data encoding device may entropyencode a sign bit separately.

The prediction residual is calculated by subtracting a prediction valuefrom the original value. For example, as represented by (Equation A7),prediction residual a2r of point a2 is calculated by subtractingpredicted value a2p of point a2 from value A₂ of attribute informationof point a2. As represented by (Equation A8), prediction residual b2r ofpoint b2 is calculated by subtracting predicted value b2p of point b2from value B₂ of attribute information of point b2.

a2r=A ₂ ·a2p  (Equation A7)

b2r=B ₂ ·b2p  (Equation A8)

In addition, the prediction residual is quantized by being divided by aQuantization Step (QS). For example, quantized value a2q of point a2 iscalculated according to (Equation A9). Quantized value b2q of point b2is calculated according to (Equation A10). Here, QS_LoD0 is a QS forLoD0, and QS_LoD1 is a QS for LoD1. In other words, a QS may be changedaccording to an LoD.

a2q=a2r/QS_LoD0  (Equation A9)

b2q=b2r/QS_LoD1  (Equation A10)

In addition, the three-dimensional data encoding device converts signedinteger values which are quantized values as indicated below intounsigned integer values as indicated below. When signed integer valuea2q is smaller than 0, the three-dimensional data encoding device setsunsigned integer value a2u to −1−(2×a2q). When signed integer value a2qis 0 or more, the three-dimensional data encoding device sets unsignedinteger value a2u to 2×a2q.

Likewise, when signed integer value b2q is smaller than 0, thethree-dimensional data encoding device sets unsigned integer value b2uto −1−(2×b2q). When signed integer value b2q is 0 or more, thethree-dimensional data encoding device sets unsigned integer value b2uto 2×b2q.

In addition, the three-dimensional data encoding device may encode thequantized prediction residual (unsigned integer value) by entropyencoding. For example, the three-dimensional data encoding device maybinarize the unsigned integer value and then apply binary arithmeticencoding to the binary value.

It is to be noted that, in this case, the three-dimensional dataencoding device may switch binarization methods according to the valueof a prediction residual. For example, when prediction residual pu issmaller than threshold value R_TH, the three-dimensional data encodingdevice binarizes prediction residual pu using a fixed bit count requiredfor representing threshold value R_TH. In addition, when predictionresidual pu is larger than or equal to threshold value R_TH, thethree-dimensional data encoding device binarizes the binary data ofthreshold value R_TH and the value of (pu−R_TH), usingexponential-Golomb coding, or the like.

For example, when threshold value R_TH is 63 and prediction residual puis smaller than 63, the three-dimensional data encoding device binarizesprediction residual pu using 6 bits. When prediction residual pu islarger than or equal to 63, the three-dimensional data encoding deviceperforms arithmetic encoding by binarizing the binary data (111111) ofthreshold value R_TH and (pu−63) using exponential-Golomb coding.

In a more specific example, when prediction residual pu is 32, thethree-dimensional data encoding device generates 6-bit binary data(100000), and arithmetic encodes the bit sequence. In addition, whenprediction residual pu is 66, the three-dimensional data encoding devicegenerates binary data (111111) of threshold value R_TH and a bitsequence (00100) representing value 3 (66-63) using exponential-Golombcoding, and arithmetic encodes the bit sequence (111111+00100).

In this way, the three-dimensional data encoding device can performencoding while preventing a binary bit count from increasing abruptly inthe case where a prediction residual becomes large by switchingbinarization methods according to the magnitude of the predictionresidual. It is to be noted that the three-dimensional data encodingdevice may add threshold value R_TH to, for example, a header of abitstream.

For example, in the case where encoding is performed at a high bit rate,that is, when a quantization scale is small, a small quantization errorand a high prediction accuracy are obtained. As a result, a predictionresidual may not be large. Thus, in this case, the three-dimensionaldata encoding device sets large threshold value R_TH. This reduces thepossibility that the binary data of threshold value R_TH is encoded,which increases the coding efficiency. In the opposite case whereencoding is performed at a low bit rate, that is, when a quantizationscale is large, a large quantization error and a low prediction accuracyare obtained. As a result, a prediction residual may be large. Thus, inthis case, the three-dimensional data encoding device sets smallthreshold value R_TH. In this way, it is possible to prevent abruptincrease in bit length of binary data.

In addition, the three-dimensional data encoding device may switchthreshold value R_TH for each LoD, and may add threshold value R_TH foreach LoD to, for example, a header. In other words, thethree-dimensional data encoding device may switch binarization methodsfor each LoD. For example, since distances between three-dimensionalpoints are large in a higher layer, a prediction accuracy is low, whichmay increase a prediction residual. Thus, the three-dimensional dataencoding device prevents abrupt increase in bit length of binary data bysetting small threshold value R_TH to the higher layer. In addition,since distances between three-dimensional points are small in a lowerlayer, a prediction accuracy is high, which may reduce a predictionresidual. Thus, the three-dimensional data encoding device increases thecoding efficiency by setting large threshold value R_TH to the lowerlayer.

FIG. 55 is a diagram indicating examples of exponential-Golomb codes.The diagram indicates the relationships between pre-binarization values(non-binary values) and post-binarization bits (codes). It is to benoted that 0 and 1 indicated in FIG. 55 may be inverted.

The three-dimensional data encoding device applies arithmetic encodingto the binary data of prediction residuals. In this way, the codingefficiency can be increased. It is to be noted that, in the applicationof the arithmetic encoding, there is a possibility that occurrenceprobability tendencies of 0 and 1 in each bit vary, in binary data,between an n-bit code which is a part binarized by n bits and aremaining code which is a part binarized using exponential-Golombcoding. Thus, the three-dimensional data encoding device may switchmethods of applying arithmetic encoding between the n-bit code and theremaining code.

For example, the three-dimensional data encoding device performsarithmetic encoding on the n-bit code using one or more coding tables(probability tables) different for each bit. At this time, thethree-dimensional data encoding device may change the number of codingtables to be used for each bit. For example, the three-dimensional dataencoding device performs arithmetic encoding using one coding table forfirst bit b0 in an n-bit code. The three-dimensional data encodingdevice uses two coding tables for next bit b1. The three-dimensionaldata encoding device switches coding tables to be used for arithmeticencoding of bit b1 according to the value (0 or 1) of b0. Likewise, thethree-dimensional data encoding device uses four coding tables for nextbit b2. The three-dimensional data encoding device switches codingtables to be used for arithmetic encoding of bit b2 according to thevalues (in a range from 0 to 3) of b0 and b1.

In this way, the three-dimensional data encoding device uses 2^(n-1)coding tables when arithmetic encoding each bit bn−1 in n-bit code. Thethree-dimensional data encoding device switches coding tables to be usedaccording to the values (occurrence patterns) of bits before bn−1. Inthis way, the three-dimensional data encoding device can use codingtables appropriate for each bit, and thus can increase the codingefficiency.

It is to be noted that the three-dimensional data encoding device mayreduce the number of coding tables to be used for each bit. For example,the three-dimensional data encoding device may switch 2^(m) codingtables according to the values (occurrence patterns) of m bits (m<n−1)before bn−1 when arithmetic encoding each bit bn−1. In this way, it ispossible to increase the coding efficiency while reducing the number ofcoding tables to be used for each bit. It is to be noted that thethree-dimensional data encoding device may update the occurrenceprobabilities of 0 and 1 in each coding table according to the values ofbinary data occurred actually. In addition, the three-dimensional dataencoding device may fix the occurrence probabilities of 0 and 1 incoding tables for some bit(s). In this way, it is possible to reduce thenumber of updates of occurrence probabilities, and thus to reduce theprocessing amount.

For example, when an n-bit code is b0, b1, b2, . . . , bn−1, the codingtable for b0 is one table (CTb0). Coding tables for b1 are two tables(CTb10 and CTb11). Coding tables to be used are switched according tothe value (0 or 1) of b0. Coding tables for b2 are four tables (CTb20,CTb21, CTb22, and CTb23). Coding tables to be used are switchedaccording to the values (in the range from 0 to 3) of b0 and b1. Codingtables for bn−1 are 2^(n-1) tables (CTbn0, CTbn1, . . . , CTbn(2^(n-1)−1)). Coding tables to be used are switched according to thevalues (in a range from 0 to 2^(n-1)−1) of b0, b1, . . . , bn−2.

It is to be noted that the three-dimensional data encoding device mayapply, to an n-bit code, arithmetic encoding (m=2^(n)) by m-ary thatsets the value in the range from 0 to 2^(n)−1 without binarization. Whenthe three-dimensional data encoding device arithmetic encodes an n-bitcode by an m-ary, the three-dimensional data decoding device mayreconstruct the n-bit code by arithmetic decoding the m-ary.

FIG. 56 is a diagram for illustrating processing in the case whereremaining codes are exponential-Golomb codes. As indicated in FIG. 56 ,each remaining code which is a part binarized using exponential-Golombcoding includes a prefix and a suffix. For example, thethree-dimensional data encoding device switches coding tables betweenthe prefix and the suffix. In other words, the three-dimensional dataencoding device arithmetic encodes each of bits included in the prefixusing coding tables for the prefix, and arithmetic encodes each of bitsincluded in the suffix using coding tables for the suffix.

It is to be noted that the three-dimensional data encoding device mayupdate the occurrence probabilities of 0 and 1 in each coding tableaccording to the values of binary data occurred actually. In addition,the three-dimensional data encoding device may fix the occurrenceprobabilities of 0 and 1 in one of coding tables. In this way, it ispossible to reduce the number of updates of occurrence probabilities,and thus to reduce the processing amount. For example, thethree-dimensional data encoding device may update the occurrenceprobabilities for the prefix, and may fix the occurrence probabilitiesfor the suffix.

In addition, the three-dimensional data encoding device decodes aquantized prediction residual by inverse quantization andreconstruction, and uses a decoded value which is the decoded predictionresidual for prediction of a current three-dimensional point to beencoded and the following three-dimensional point(s). More specifically,the three-dimensional data encoding device calculates an inversequantized value by multiplying the quantized prediction residual(quantized value) with a quantization scale, and adds the inversequantized value and a prediction value to obtain the decoded value(reconstructed value).

For example, quantized value a2iq of point a2 is calculated usingquantized value a2q of point a2 according to (Equation A11). Inversequantized value b2iq of point b2q is calculated using quantized valueb2q of point b2 according to (Equation A12). Here, QS_LoD0 is a QS forLoD0, and QS_LoD1 is a QS for LoD1. In other words, a QS may be changedaccording to an LoD.

a2iq=a2q×QS_LoD0  (Equation A11)

b2iq=b2q×QS_LoD1  (Equation A12)

For example, as represented by (Equation A13), decoded value a2rec ofpoint a2 is calculated by adding inverse quantization value a2iq ofpoint a2 to predicted value a2p of point a2. As represented by (EquationA14), decoded value b2rec of point b2 is calculated by adding inversequantized value b2iq of point b2 to predicted value b2p of point b2.

a2rec=a2iq+a2p  (Equation A13)

b2rec=b2iq+b2p  (Equation A14)

Hereinafter, a syntax example of a bitstream according to the presentembodiment is described. FIG. 57 is a diagram indicating the syntaxexample of an attribute header (attribute_header) according to thepresent embodiment.

The attribute header is header information of attribute information. Asindicated in FIG. 57 , the attribute header includes the number oflayers information (NumLoD), the number of three-dimensional pointsinformation (NumOfPoint[i]), a layer threshold value (Thres_LoD[i]), thenumber of neighbor points information (NumNeighborPoint[i]), aprediction threshold value (THd[i]), a quantization scale (QS[i]), and abinarization threshold value (R_TH[i]).

The number of layers information (NumLoD) indicates the number of LoDsto be used.

The number of three-dimensional points information (NumOfPoint[i])indicates the number of three-dimensional points belonging to layer i.It is to be noted that the three-dimensional data encoding device mayadd, to another header, the number of three-dimensional pointsinformation indicating the total number of three-dimensional points. Inthis case, the three-dimensional data encoding device does not need toadd, to a header, NumOfPoint [NumLoD−1] indicating the number ofthree-dimensional points belonging to the lowermost layer. In this case,the three-dimensional data decoding device is capable of calculatingNumOfPoint [NumLoD−1] according to (Equation A15). In this case, it ispossible to reduce the code amount of the header.

$\begin{matrix}{\lbrack {{Math}.4} \rbrack} &  \\{{{NumOfPoint}\lbrack {{NumLoD} - 1} \rbrack} = \text{ }{{AllNumOfPoint} - {\sum\limits_{j = 0}^{{NumLoD} - 2}{{NumOfPoint}\lbrack j\rbrack}}}} & ( {{Equation}{A15}} )\end{matrix}$

The layer threshold value (Thres_LoD[i]) is a threshold value to be usedto set layer i. The three-dimensional data encoding device and thethree-dimensional data decoding device configure LoDi in such a mannerthat the distance between points in LoDi becomes larger than thresholdvalue Thres_LoD[i]. The three-dimensional data encoding device does notneed to add the value of Thres_LoD [NumLoD−1] (lowermost layer) to aheader. In this case, the three-dimensional data decoding device mayestimate 0 as the value of Thres_LoD [NumLoD−1]. In this case, it ispossible to reduce the code amount of the header.

The number of neighbor points information (NumNeighborPoint[i])indicates the upper limit value of the number of neighbor points to beused to generate a predicted value of a three-dimensional pointbelonging to layer i. The three-dimensional data encoding device maycalculate a predicted value using the number of neighbor points M whenthe number of neighbor points M is smaller than NumNeighborPoint[i](M<NumNeighborPoint[i]). Furthermore, when there is no need todifferentiate the values of NumNeighborPoint[i] for respective LoDs, thethree-dimensional data encoding device may add a piece of the number ofneighbor points information (NumNeighborPoint) to be used in all LoDs toa header.

The prediction threshold value (THd[i]) indicates the upper limit valueof the distance between a current three-dimensional point to be encodedor decoded in layer i and each of neighbor three-dimensional points tobe used to predict the current three-dimensional point. Thethree-dimensional data encoding device and the three-dimensional datadecoding device do not use, for prediction, any three-dimensional pointdistant from the current three-dimensional point over THd[i]. It is tobe noted that, when there is no need to differentiate the values ofTHd[i] for respective LoDs, the three-dimensional data encoding devicemay add a single prediction threshold value (THd) to be used in all LoDsto a header.

The quantization scale (QS[i]) indicates a quantization scale to be usedfor quantization and inverse quantization in layer i.

The binarization threshold value (R_TH[i]) is a threshold value forswitching binarization methods of prediction residuals ofthree-dimensional points belonging to layer i. For example, thethree-dimensional data encoding device binarizes prediction residual puusing a fixed bit count when a prediction residual is smaller thanthreshold value R_TH, and binarizes the binary data of threshold valueR_TH and the value of (pu−R_TH) using exponential-Golomb coding when aprediction residual is larger than or equal to threshold value R_TH. Itis to be noted that, when there is no need to switch the values ofR_TH[i] between LoDs, the three-dimensional data encoding device may adda single binarization threshold value (R_TH) to be used in all LoDs to aheader.

It is to be noted that R_TH[i] may be the maximum value which can berepresented by n bits. For example, R_TH is 63 in the case of 6 bits,and R_TH is 255 in the case of 8 bits. Alternatively, thethree-dimensional data encoding device may encode a bit count instead ofencoding the maximum value which can be represented by n bits as abinarization threshold value. For example, the three-dimensional dataencoding device may add value 6 in the case of R_TH[i]=63 to a header,and may add value 8 in the case of R_TH[i]=255 to a header.Alternatively, the three-dimensional data encoding device may define theminimum value (minimum bit count) representing R_TH[i], and add arelative bit count from the minimum value to a header. For example, thethree-dimensional data encoding device may add value 0 to a header whenR_TH[i]=63 is satisfied and the minimum bit count is 6, and may addvalue 2 to a header when R_TH[i]=255 is satisfied and the minimum bitcount is 6.

Alternatively, the three-dimensional data encoding device may entropyencode at least one of NumLoD, Thres_LoD[i], NumNeighborPoint[i],THd[i], QS[i], and R_TH[i], and add the entropy encoded one to a header.For example, the three-dimensional data encoding device may binarizeeach value and perform arithmetic encoding on the binary value. Inaddition, the three-dimensional data encoding device may encode eachvalue using a fixed length in order to reduce the processing amount.

Alternatively, the three-dimensional data encoding device does notalways need to add at least one of NumLoD, Thres_LoD[i],NumNeighborPoint[i], THd[i], QS[i], and R_TH[i] to a header. Forexample, at least one of these values may be defined by a profile or alevel in a standard, or the like. In this way, it is possible to reducethe bit amount of the header.

FIG. 58 is a diagram indicating the syntax example of attribute data(attribute_data) according to the present embodiment. The attribute dataincludes encoded data of the attribute information of a plurality ofthree-dimensional points. As indicated in FIG. 58 , the attribute dataincludes an n-bit code and a remaining code.

The n-bit code is encoded data of a prediction residual of a value ofattribute information or a part of the encoded data. The bit length ofthe n-bit code depends on value R_TH[i]. For example, the bit length ofthe n-bit code is 6 bits when the value indicated by R_TH[i] is 63, thebit length of the n-bit code is 8 bits when the value indicated byR_TH[i] is 255.

The remaining code is encoded data encoded using exponential-Golombcoding among encoded data of the prediction residual of the value of theattribute information. The remaining code is encoded or decoded when thevalue of the n-bit code is equal to R_TH[i]. The three-dimensional datadecoding device decodes the prediction residual by adding the value ofthe n-bit code and the value of the remaining code. It is to be notedthat the remaining code does not always need to be encoded or decodedwhen the value of the n-bit code is not equal to R_TH[i].

Hereinafter, a description is given of a flow of processing in thethree-dimensional data encoding device. FIG. 59 is a flowchart of athree-dimensional data encoding process performed by thethree-dimensional data encoding device.

First, the three-dimensional data encoding device encodes geometryinformation (geometry) (S3001). For example, the three-dimensional dataencoding is performed using octree representation.

When the positions of three-dimensional points changed by quantization,etc, after the encoding of the geometry information, thethree-dimensional data encoding device re-assigns attribute informationof the original three-dimensional points to the post-changethree-dimensional points (S3002). For example, the three-dimensionaldata encoding device interpolates values of attribute informationaccording to the amounts of change in position to re-assign theattribute information. For example, the three-dimensional data encodingdevice detects pre-change N three-dimensional points closer to thepost-change three-dimensional positions, and performs weighted averagingof the values of attribute information of the N three-dimensionalpoints. For example, the three-dimensional data encoding devicedetermines weights based on distances from the post-changethree-dimensional positions to the respective N three-dimensionalpositions in weighted averaging. The three-dimensional data encodingdevice then determines the values obtained through the weightedaveraging to be the values of the attribute information of thepost-change three-dimensional points. When two or more of thethree-dimensional points are changed to the same three-dimensionalposition through quantization, etc., the three-dimensional data encodingdevice may assign the average value of the attribute information of thepre-change two or more three-dimensional points as the values of theattribute information of the post-change three-dimensional points.

Next, the three-dimensional data encoding device encodes the attributeinformation (attribute) re-assigned (S3003). For example, when encodinga plurality of kinds of attribute information, the three-dimensionaldata encoding device may encode the plurality of kinds of attributeinformation in order. For example, when encoding colors and reflectancesas attribute information, the three-dimensional data encoding device maygenerate a bitstream added with the color encoding results and thereflectance encoding results after the color encoding results. It is tobe noted that the order of the plurality of encoding results ofattribute information to be added to a bitstream is not limited to theorder, and may be any order.

Alternatively, the three-dimensional data encoding device may add, to aheader for example, information indicating the start location of encodeddata of each attribute information in a bitstream. In this way, thethree-dimensional data decoding device is capable of selectivelydecoding attribute information required to be decoded, and thus iscapable of skipping the decoding process of the attribute informationnot required to be decoded. Accordingly, it is possible to reduce theamount of processing by the three-dimensional data decoding device.Alternatively, the three-dimensional data encoding device may encode aplurality of kinds of attribute information in parallel, and mayintegrate the encoding results into a single bitstream. In this way, thethree-dimensional data encoding device is capable of encoding theplurality of kinds of attribute information at high speed.

FIG. 60 is a flowchart of an attribute information encoding process(S3003). First, the three-dimensional data encoding device sets LoDs(S3011). In other words, the three-dimensional data encoding deviceassigns each of three-dimensional points to any one of the plurality ofLoDs.

Next, the three-dimensional data encoding device starts a loop for eachLoD (S3012). In other words, the three-dimensional data encoding deviceiteratively performs the processes of Steps from S3013 to S3021 for eachLoD.

Next, the three-dimensional data encoding device starts a loop for eachthree-dimensional point (S3013). In other words, the three-dimensionaldata encoding device iteratively performs the processes of Steps fromS3014 to S3020 for each three-dimensional point.

First, the three-dimensional data encoding device searches a pluralityof neighbor points which are three-dimensional points present in theneighborhood of a current three-dimensional point to be processed andare to be used to calculate a predicted value of the currentthree-dimensional point (S3014). Next, the three-dimensional dataencoding device calculates the weighted average of the values ofattribute information of the plurality of neighbor points, and sets theresulting value to predicted value P (S3015). Next, thethree-dimensional data encoding device calculates a prediction residualwhich is the difference between the attribute information of the currentthree-dimensional point and the predicted value (S3016). Next, thethree-dimensional data encoding device quantizes the prediction residualto calculate a quantized value (S3017). Next, the three-dimensional dataencoding device arithmetic encodes the quantized value (S3018).

Next, the three-dimensional data encoding device inverse quantizes thequantized value to calculate an inverse quantized value (S3019). Next,the three-dimensional data encoding device adds a prediction value tothe inverse quantized value to generate a decoded value (S3020). Next,the three-dimensional data encoding device ends the loop for eachthree-dimensional point (S3021). Next, the three-dimensional dataencoding device ends the loop for each LoD (S3022).

Hereinafter, a description is given of a three-dimensional data decodingprocess in the three-dimensional data decoding device which decodes abitstream generated by the three-dimensional data encoding device.

The three-dimensional data decoding device generates decoded binary databy arithmetic decoding the binary data of the attribute information inthe bitstream generated by the three-dimensional data encoding device,according to the method similar to the one performed by thethree-dimensional data encoding device. It is to be noted that whenmethods of applying arithmetic encoding are switched between the part(n-bit code) binarized using n bits and the part (remaining code)binarized using exponential-Golomb coding in the three-dimensional dataencoding device, the three-dimensional data decoding device performsdecoding in conformity with the arithmetic encoding, when applyingarithmetic decoding.

For example, the three-dimensional data decoding device performsarithmetic decoding using coding tables (decoding tables) different foreach bit in the arithmetic decoding of the n-bit code. At this time, thethree-dimensional data decoding device may change the number of codingtables to be used for each bit. For example, the three-dimensional datadecoding device performs arithmetic decoding using one coding table forfirst bit b0 in the n-bit code. The three-dimensional data decodingdevice uses two coding tables for next bit b1. The three-dimensionaldata decoding device switches coding tables to be used for arithmeticdecoding of bit b1 according to the value (0 or 1) of b0. Likewise, thethree-dimensional data decoding device uses four coding tables for nextbit b2. The three-dimensional data decoding device switches codingtables to be used for arithmetic decoding of bit b2 according to thevalues (in the range from 0 to 3) of b0 and b1.

In this way, the three-dimensional data decoding device uses 2^(n-1)coding tables when arithmetic decoding each bit bn−1 in the n-bit code.The three-dimensional data decoding device switches coding tables to beused according to the values (occurrence patterns) of bits before bn−1.In this way, the three-dimensional data decoding device is capable ofappropriately decoding a bitstream encoded at an increased codingefficiency using the coding tables appropriate for each bit.

It is to be noted that the three-dimensional data decoding device mayreduce the number of coding tables to be used for each bit. For example,the three-dimensional data decoding device may switch 2^(m) codingtables according to the values (occurrence patterns) of m bits (m<n−1)before bn−1 when arithmetic decoding each bit bn−1. In this way, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream encoded at the increased coding efficiency whilereducing the number of coding tables to be used for each bit. It is tobe noted that the three-dimensional data decoding device may update theoccurrence probabilities of 0 and 1 in each coding table according tothe values of binary data occurred actually. In addition, thethree-dimensional data decoding device may fix the occurrenceprobabilities of 0 and 1 in coding tables for some bit(s). In this way,it is possible to reduce the number of updates of occurrenceprobabilities, and thus to reduce the processing amount.

For example, when an n-bit code is b0, b1, b2, . . . , bn−1, the codingtable for b0 is one (CTb0). Coding tables for b1 are two tables (CTb10and CTb11). Coding tables to be used are switched according to the value(0 or 1) of b0. Coding tables for b2 are four tables (CTb20, CTb21,CTb22, and CTb23). Coding tables to be used according to the values (inthe range from 0 to 3) of b0 and b1. Coding tables for bn−1 are 2n^(n-1)tables (CTbn0, CTbn1, . . . , CTbn (2^(n-1)−1)). Coding tables to beused are switched according to the values (in the range from 0 to2^(n-1)−1) of b0, b1, . . . , bn−2.

FIG. 61 is a diagram for illustrating processing in the case whereremaining codes are exponential-Golomb codes. As indicated in FIG. 61 ,the part (remaining part) binarized and encoded by the three-dimensionaldata encoding device using exponential-Golomb coding includes a prefixand a suffix. For example, the three-dimensional data decoding deviceswitches coding tables between the prefix and the suffix. In otherwords, the three-dimensional data decoding device arithmetic decodeseach of bits included in the prefix using coding tables for the prefix,and arithmetic decodes each of bits included in the suffix using codingtables for the suffix.

It is to be noted that the three-dimensional data decoding device mayupdate the occurrence probabilities of 0 and 1 in each coding tableaccording to the values of binary data occurred at the time of decoding.In addition, the three-dimensional data decoding device may fix theoccurrence probabilities of 0 and 1 in one of coding tables. In thisway, it is possible to reduce the number of updates of occurrenceprobabilities, and thus to reduce the processing amount. For example,the three-dimensional data decoding device may update the occurrenceprobabilities for the prefix, and may fix the occurrence probabilitiesfor the suffix.

Furthermore, the three-dimensional data decoding device decodes thequantized prediction residual (unsigned integer value) by debinarizingthe binary data of the prediction residual arithmetic decoded accordingto a method in conformity with the encoding method used by thethree-dimensional data encoding device. The three-dimensional datadecoding device first arithmetic decodes the binary data of an n-bitcode to calculate a value of the n-bit code. Next, the three-dimensionaldata decoding device compares the value of the n-bit code with thresholdvalue R_TH.

In the case where the value of the n-bit code and threshold value R_THmatch, the three-dimensional data decoding device determines that a bitencoded using exponential-Golomb coding is present next, and arithmeticdecodes the remaining code which is the binary data encoded usingexponential-Golomb coding. The three-dimensional data decoding devicethen calculates, from the decoded remaining code, a value of theremaining code using a reverse lookup table indicating the relationshipbetween the remaining code and the value. FIG. 62 is a diagramindicating an example of a reverse lookup table indicating relationshipsbetween remaining codes and the values thereof. Next, thethree-dimensional data decoding device adds the obtained value of theremaining code to R_TH, thereby obtaining a debinarized quantizedprediction residual.

In the opposite case where the value of the n-bit code and thresholdvalue R_TH do not match (the value of the n-bit code is smaller thanvalue R_TH), the three-dimensional data decoding device determines thevalue of the n-bit code to be the debinarized quantized predictionresidual as it is. In this way, the three-dimensional data decodingdevice is capable of appropriately decoding the bitstream generatedwhile switching the binarization methods according to the values of theprediction residuals by the three-dimensional data encoding device.

It is to be noted that, when threshold value R_TH is added to, forexample, a header of a bitstream, the three-dimensional data decodingdevice may decode threshold value R_TH from the header, and may switchdecoding methods using decoded threshold value R_TH. When thresholdvalue R_TH is added to, for example, a header for each LoD, thethree-dimensional data decoding device switch decoding methods usingthreshold value R_TH decoded for each LoD.

For example, when threshold value R_TH is 63 and the value of thedecoded n-bit code is 63, the three-dimensional data decoding devicedecodes the remaining code using exponential-Golomb coding, therebyobtaining the value of the remaining code. For example, in the exampleindicated in FIG. 62 , the remaining code is 00100, and 3 is obtained asthe value of the remaining code. Next, the three-dimensional datadecoding device adds 63 that is threshold value R_TH and 3 that is thevalue of the remaining code, thereby obtaining 66 that is the value ofthe prediction residual.

In addition, when the value of the decoded n-bit code is 32, thethree-dimensional data decoding device sets 32 that is the value of then-bit code to the value of the prediction residual.

In addition, the three-dimensional data decoding device converts thedecoded quantized prediction residual, for example, from an unsignedinteger value to a signed integer value, through processing inverse tothe processing in the three-dimensional data encoding device. In thisway, when entropy decoding the prediction residual, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream generated without considering occurrence of anegative integer. It is to be noted that the three-dimensional datadecoding device does not always need to convert an unsigned integervalue to a signed integer value, and that, for example, thethree-dimensional data decoding device may decode a sign bit whendecoding a bitstream generated by separately entropy encoding the signbit.

The three-dimensional data decoding device performs decoding by inversequantizing and reconstructing the quantized prediction residual afterbeing converted to the signed integer value, to obtain a decoded value.The three-dimensional data decoding device uses the generated decodedvalue for prediction of a current three-dimensional point to be decodedand the following three-dimensional point(s). More specifically, thethree-dimensional data decoding device multiplies the quantizedprediction residual by a decoded quantization scale to calculate aninverse quantized value and adds the inverse quantized value and thepredicted value to obtain the decoded value.

The decoded unsigned integer value (unsigned quantized value) isconverted into a signed integer value through the processing indicatedbelow. When the least significant bit (LSB) of decoded unsigned integervalue a2u is 1, the three-dimensional data decoding device sets signedinteger value a2q to −((a2u+1)>>1). When the LSB of unsigned integervalue a2u is not 1, the three-dimensional data decoding device setssigned integer value a2q to ((a2u>>1).

Likewise, when an LSB of decoded unsigned integer value b2u is 1, thethree-dimensional data decoding device sets signed integer value b2q to−((b2u+1)>>1). When the LSB of decoded unsigned integer value n2u is not1, the three-dimensional data decoding device sets signed integer valueb2q to ((b2u>>1).

Details of the inverse quantization and reconstruction processing by thethree-dimensional data decoding device are similar to the inversequantization and reconstruction processing in the three-dimensional dataencoding device.

Hereinafter, a description is given of a flow of processing in thethree-dimensional data decoding device. FIG. 63 is a flowchart of athree-dimensional data decoding process performed by thethree-dimensional data decoding device. First, the three-dimensionaldata decoding device decodes geometry information (geometry) from abitstream (S3031). For example, the three-dimensional data decodingdevice performs decoding using octree representation.

Next, the three-dimensional data decoding device decodes attributeinformation (attribute) from the bitstream (S3032). For example, whendecoding a plurality of kinds of attribute information, thethree-dimensional data decoding device may decode the plurality of kindsof attribute information in order. For example, when decoding colors andreflectances as attribute information, the three-dimensional datadecoding device decodes the color encoding results and the reflectanceencoding results in order of assignment in the bitstream. For example,when the reflectance encoding results are added after the color encodingresults in a bitstream, the three-dimensional data decoding devicedecodes the color encoding results, and then decodes the reflectanceencoding results. It is to be noted that the three-dimensional datadecoding device may decode, in any order, the encoding results of theattribute information added to the bitstream.

Alternatively, the three-dimensional data encoding device may add, to aheader for example, information indicating the start location of encodeddata of each attribute information in a bitstream. In this way, thethree-dimensional data decoding device is capable of selectivelydecoding attribute information required to be decoded, and thus iscapable of skipping the decoding process of the attribute informationnot required to be decoded. Accordingly, it is possible to reduce theamount of processing by the three-dimensional data decoding device. Inaddition, the three-dimensional data decoding device may decode aplurality of kinds of attribute information in parallel, and mayintegrate the decoding results into a single three-dimensional pointcloud. In this way, the three-dimensional data decoding device iscapable of decoding the plurality of kinds of attribute information athigh speed.

FIG. 64 is a flowchart of an attribute information decoding process(S3032). First, the three-dimensional data decoding device sets LoDs(S3041). In other words, the three-dimensional data decoding deviceassigns each of three-dimensional points having the decoded geometryinformation to any one of the plurality of LoDs. For example, thisassignment method is the same as the assignment method used in thethree-dimensional data encoding device.

Next, the three-dimensional data decoding device starts a loop for eachLoD (S3042). In other words, the three-dimensional data decoding deviceiteratively performs the processes of Steps from S3043 to S3049 for eachLoD.

Next, the three-dimensional data decoding device starts a loop for eachthree-dimensional point (S3043). In other words, the three-dimensionaldata decoding device iteratively performs the processes of Steps fromS3044 to S3048 for each three-dimensional point.

First, the three-dimensional data decoding device searches a pluralityof neighbor points which are three-dimensional points present in theneighborhood of a current three-dimensional point to be processed andare to be used to calculate a predicted value of the currentthree-dimensional point to be processed (S3044). Next, thethree-dimensional data decoding device calculates the weighted averageof the values of attribute information of the plurality of neighborpoints, and sets the resulting value to predicted value P (S3045). It isto be noted that these processes are similar to the processes in thethree-dimensional data encoding device.

Next, the three-dimensional data decoding device arithmetic decodes thequantized value from the bitstream (S3046). The three-dimensional datadecoding device inverse quantizes the decoded quantized value tocalculate an inverse quantized value (S3047). Next, thethree-dimensional data decoding device adds a predicted value to theinverse quantized value to generate a decoded value (S3048). Next, thethree-dimensional data decoding device ends the loop for eachthree-dimensional point (S3049). Next, the three-dimensional dataencoding device ends the loop for each LoD (S3050).

The following describes configurations of the three-dimensional dataencoding device and three-dimensional data decoding device according tothe present embodiment. FIG. 65 is a block diagram illustrating aconfiguration of three-dimensional data encoding device 3000 accordingto the present embodiment. Three-dimensional data encoding device 3000includes geometry information encoder 3001, attribute informationre-assigner 3002, and attribute information encoder 3003.

Attribute information encoder 3003 encodes geometry information(geometry) of a plurality of three-dimensional points included in aninput point cloud. Attribute information re-assigner 3002 re-assigns thevalues of attribute information of the plurality of three-dimensionalpoints included in the input point cloud, using the encoding anddecoding results of the geometry information. Attribute informationencoder 3003 encodes the re-assigned attribute information (attribute).Furthermore, three-dimensional data encoding device 3000 generates abitstream including the encoded geometry information and the encodedattribute information.

FIG. 66 is a block diagram illustrating a configuration ofthree-dimensional data decoding device 3010 according to the presentembodiment. Three-dimensional data decoding device 3010 includesgeometry information decoder 3011 and attribute information decoder3012.

Geometry information decoder 3011 decodes the geometry information(geometry) of a plurality of three-dimensional points from a bitstream.Attribute information decoder 3012 decodes the attribute information(attribute) of the plurality of three-dimensional points from thebitstream. Furthermore, three-dimensional data decoding device 3010integrates the decoded geometry information and the decoded attributeinformation to generate an output point cloud.

As described above, the three-dimensional data encoding device accordingto the present embodiment performs the process illustrated in FIG. 67 .The three-dimensional data encoding device encodes a three-dimensionalpoint having attribute information. First, the three-dimensional dataencoding device calculates a predicted value of the attributeinformation of the three-dimensional point (S3061). Next, thethree-dimensional data encoding device calculates a prediction residualwhich is the difference between the attribute information of thethree-dimensional point and the predicted value (S3062). Next, thethree-dimensional data encoding device binarizes the prediction residualto generate binary data (S3063). Next, the three-dimensional dataencoding device arithmetic encodes the binary data (S3064).

In this way, the three-dimensional data encoding device is capable ofreducing the code amount of the to-be-coded data of the attributeinformation by calculating the prediction residual of the attributeinformation, and binarizing and arithmetic encoding the predictionresidual.

For example, in arithmetic encoding (S3064), the three-dimensional dataencoding device uses coding tables different for each of bits of binarydata. By doing so, the three-dimensional data encoding device canincrease the coding efficiency.

For example, in arithmetic encoding (S3064), the number of coding tablesto be used is larger for a lower-order bit of the binary data.

For example, in arithmetic encoding (S3064), the three-dimensional dataencoding device selects coding tables to be used to arithmetic encode acurrent bit included in binary data, according to the value of ahigher-order bit with respect to the current bit. By doing so, since thethree-dimensional data encoding device can select coding tables to beused according to the value of the higher-order bit, thethree-dimensional data encoding device can increase the codingefficiency.

For example, in binarization (S3063), the three-dimensional dataencoding device: binarizes a prediction residual using a fixed bit countto generate binary data when the prediction residual is smaller than athreshold value (R_TH); and generates binary data including a first code(n-bit code) and a second code (remaining code) when the predictionresidual is larger than or equal to the threshold value (R_TH). Thefirst code is of a fixed bit count indicating the threshold value(R_TH), and the second code (remaining code) is obtained by binarizing,using exponential-Golomb coding, the value obtained by subtracting thethreshold value (R_TH) from the prediction residual. In arithmeticencoding (S3064), the three-dimensional data encoding device usesarithmetic encoding methods different between the first code and thesecond code.

With this, for example, since it is possible to arithmetic encode thefirst code and the second code using arithmetic encoding methodsrespectively suitable for the first code and the second code, it ispossible to increase coding efficiency.

For example, the three-dimensional data encoding device quantizes theprediction residual, and, in binarization (S3063), binarizes thequantized prediction residual. The threshold value (R_TH) is changedaccording to a quantization scale in quantization. With this, since thethree-dimensional data encoding device can use the threshold valuesuitably according to the quantization scale, it is possible to increasethe coding efficiency.

For example, the second code includes a prefix and a suffix. Inarithmetic encoding (S3064), the three-dimensional data encoding deviceuses different coding tables between the prefix and the suffix. In thisway, the three-dimensional data encoding device can increase the codingefficiency.

For example, the three-dimensional data encoding device includes aprocessor and memory, and the processor performs the above process usingthe memory.

The three-dimensional data decoding device according to the presentembodiment performs the process illustrated in FIG. 68 . Thethree-dimensional data decoding device decodes a three-dimensional pointhaving attribute information. First, the three-dimensional data decodingdevice calculates a predicted value of the attribute information of athree-dimensional point (S3071). Next, the three-dimensional datadecoding device arithmetic decodes encoded data included in a bitstreamto generate binary data (S3072). Next, the three-dimensional datadecoding device debinarizes the binary data to generate a predictionresidual (S3073). Next, the three-dimensional data decoding devicecalculates a decoded value of the attribute information of thethree-dimensional point by adding the predicted value and the predictionresidual (S3074).

In this way, the three-dimensional data decoding device is capable ofappropriately decoding the bitstream of the attribute informationgenerated by calculating the prediction residual of the attributeinformation and binarizing and arithmetic decoding the predictionresidual.

For example, in arithmetic decoding (S3072), the three-dimensional datadecoding device uses coding tables different for each of bits of binarydata. With this, the three-dimensional data decoding device is capableof appropriately decoding the bitstream encoded at an increased codingefficiency.

For example, in arithmetic decoding (S3072), the number of coding tablesto be used is larger for a lower bit of the binary data.

For example, in arithmetic decoding (S3072), the three-dimensional datadecoding device selects coding tables to be used to arithmetic decode acurrent bit included in binary data, according to the value of ahigher-order bit with respect to the current bit. With this, thethree-dimensional data decoding device is capable of appropriatelydecoding the bitstream encoded at an increased coding efficiency.

For example, in debinarizaion (S3073), the three-dimensional datadecoding device debinarizes the first code (n-bit code) of a fixed bitcount included in the binary data to generate a first value. Thethree-dimensional data decoding device: determines the first value to bethe prediction residual when the first value is smaller than thethreshold value (R_TH); and, when the first value is larger than orequal to the threshold value (R_YH), generates a second value bydebinarizing the second code (remaining code) which is anexponential-Golomb code included in the binary data and adds the firstvalue and the second value, thereby generating a prediction residual. Inthe arithmetic decoding (S3072), the three-dimensional data decodingdevice uses arithmetic decoding methods different between the first codeand the second code.

With this, the three-dimensional data decoding device is capable ofappropriately decoding the bitstream encoded at an increased codingefficiency.

For example, the three dimensional data decoding device inversequantizes the prediction residual, and, in addition (S3074), adds thepredicted value and the inverse quantized prediction residual. Thethreshold value (R_TH) is changed according to a quantization scale ininverse quantization. With this, the three-dimensional data decodingdevice is capable of appropriately decoding the bitstream encoded at anincreased coding efficiency.

For example, the second code includes a prefix and a suffix. Inarithmetic decoding (S3072), the three-dimensional data decoding deviceuses different coding tables between the prefix and the suffix. Withthis, the three-dimensional data decoding device is capable ofappropriately decoding the bitstream encoded at an increased codingefficiency.

For example, the three-dimensional data decoding device includes aprocessor and memory, and the processor performs the above-describedprocess using the memory.

Embodiment 9

Predicted values may be generated by a method different from that inEmbodiment 8. Hereinafter, a three-dimensional point to be encoded isreferred to as a first three-dimensional point, and one or morethree-dimensional points in the vicinity of the first three-dimensionalpoint is referred to as one or more second three-dimensional points insome cases.

For example, in generating of a predicted value of an attributeinformation item (attribute information) of a three-dimensional point,an attribute value as it is of a closest three-dimensional point amongencoded and decoded three-dimensional points in the vicinity of athree-dimensional point to be encoded may be generated as a predictedvalue. In the generating of the predicted value, prediction modeinformation (PredMode) may be appended for each three-dimensional point,and one predicted value may be selected from a plurality of predictedvalues to allow generation of a predicted value. Specifically, forexample, it is conceivable that, for total number M of prediction modes,an average value is assigned to prediction mode 0, an attribute value ofthree-dimensional point A is assigned to prediction mode 1, . . . , andan attribute value of three-dimensional point Z is assigned toprediction mode M−1, and the prediction mode used for prediction isappended to a bitstream for each three-dimensional point. As such, afirst prediction mode value indicating a first prediction mode forcalculating, as a predicted value, an average of attribute informationitems of the surrounding three-dimensional points may be smaller than asecond prediction mode value indicating a second prediction mode forcalculating, as a predicted value, an attribute information item as itis of a surrounding three-dimensional point. Here, the “average value”as the predicted value calculated in prediction mode 0 is an averagevalue of the attribute values of the three-dimensional points in thevicinity of the three-dimensional point to be encoded.

FIG. 69 is a diagram showing a first example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9. FIG. 70 is a diagram showing examples of attributeinformation items used as the predicted values according to Embodiment9. FIG. 71 is a diagram showing a second example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9.

Number M of prediction modes may be appended to a bitstream. Number M ofprediction modes may be defined by a profile or a level of standardsrather than appended to the bitstream. Number M of prediction modes maybe also calculated from number N of three-dimensional points used forprediction. For example, number M of prediction modes may be calculatedby M=N+1.

The table in FIG. 69 is an example of a case with number N ofthree-dimensional points used for prediction being 4 and number M ofprediction modes being 5. A predicted value of an attribute informationitem of point b2 can be generated by using attribute information itemsof points a0, a1, a2, b1. In selecting one prediction mode from aplurality of prediction modes, a prediction mode for generating, as apredicted value, an attribute value of each of points a0, a1, a2, b1 maybe selected in accordance with distance information from point b2 toeach of points a0, a1, a2, b1. The prediction mode is appended for eachthree-dimensional point to be encoded. The predicted value is calculatedin accordance with a value corresponding to the appended predictionmode.

The table in FIG. 71 is, as in FIG. 69 , an example of a case withnumber N of three-dimensional points used for prediction being 4 andnumber M of prediction modes being 5. A predicted value of an attributeinformation item of point a2 can be generated by using attributeinformation items of points a0, a1. In selecting one prediction modefrom a plurality of prediction modes, a prediction mode for generating,as a predicted value, an attribute value of each of points a0 and a1 maybe selected in accordance with distance information from point a2 toeach of points a0, a1. The prediction mode is appended for eachthree-dimensional point to be encoded. The predicted value is calculatedin accordance with a value corresponding to the appended predictionmode.

When the number of neighboring points, that is, number N of surroundingthree-dimensional points is smaller than four such as at point a2 above,a prediction mode to which a predicted value is not assigned may bewritten as “not available” in the table.

Assignment of values of the prediction modes may be determined inaccordance with the distance from the three-dimensional point to beencoded. For example, prediction mode values indicating a plurality ofprediction modes decrease with decreasing distance from thethree-dimensional point to be encoded to the surroundingthree-dimensional points having the attribute information items used asthe predicted values. The example in FIG. 69 shows that points b1, a2,a1, a0 are sequentially located closer to point b2 as thethree-dimensional point to be encoded. For example, in the calculatingof the predicted value, the attribute information item of point b1 iscalculated as the predicted value in a prediction mode indicated by aprediction mode value of “1” among two or more prediction modes, and theattribute information item of point a2 is calculated as the predictedvalue in a prediction mode indicated by a prediction mode value of “2”.As such, the prediction mode value indicating the prediction mode forcalculating, as the predicted value, the attribute information item ofpoint b1 is smaller than the prediction mode value indicating theprediction mode for calculating, as the predicted value, the attributeinformation item of point a2 farther from point b2 than point b1.

Thus, a small prediction mode value can be assigned to a point that ismore likely to be predicted and selected due to a short distance,thereby reducing a bit number for encoding the prediction mode value.Also, a small prediction mode value may be preferentially assigned to athree-dimensional point belonging to the same LoD as thethree-dimensional point to be encoded.

FIG. 72 is a diagram showing a third example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9. Specifically, the third example is an example of a casewhere an attribute information item used as a predicted value is a valueof color information (YUV) of a surrounding three-dimensional point. Assuch, the attribute information item used as the predicted value may becolor information indicating a color of the three-dimensional point.

As shown in FIG. 72 , a predicted value calculated in a prediction modeindicated by a prediction mode value of “0” is an average of Y, U, and Vcomponents defining a YUV color space. Specifically, the predicted valueincludes a weighted average Yave of Y component values Yb1, Ya2, Ya1,Ya0 corresponding to points b1, a2, a1, a0, respectively, a weightedaverage Uave of U component values Ub1, Ua2, Ua1, Ua0 corresponding topoints b1, a2, a1, a0, respectively, and a weighted average Vave of Vcomponent values Vb1, Va2, Va1, Va0 corresponding to points b1, a2, a1,a0, respectively. Predicted values calculated in prediction modesindicated by prediction mode values of “1” to “4” include colorinformation of the surrounding three-dimensional points b1, a2, a1, a0.The color information is indicated by combinations of the Y, U, and Vcomponent values.

In FIG. 72 , the color information is indicated by a value defined bythe YUV color space, but not limited to the YUV color space. The colorinformation may be indicated by a value defined by an RGB color space ora value defined by any other color space.

As such, in the calculating of the predicted value, two or more averagesor two or more attribute information items may be calculated as thepredicted values of the prediction modes. The two or more averages orthe two or more attribute information items may indicate two or morecomponent values each defining a color space.

For example, when a prediction mode indicated by a prediction mode valueof “2” in the table in FIG. 72 is selected, a Y component, a Ucomponent, and a V component as attribute values of thethree-dimensional point to be encoded may be encoded as predicted valuesYa2, Ua2, Va2. In this case, the prediction mode value of “2” isappended to the bitstream.

FIG. 73 is a diagram showing a fourth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9. Specifically, the fourth example is an example of a casewhere an attribute information item used as a predicted value is a valueof reflectance information of a surrounding three-dimensional point. Thereflectance information is, for example, information indicatingreflectance R.

As shown in FIG. 73 , a predicted value calculated in a prediction modeindicated by a prediction mode value of “0” is weighted average Rave ofreflectances Rb1, Ra2, Ra1, Ra0 corresponding to points b1, a2, a1, a0,respectively. Predicted values calculated in prediction modes indicatedby prediction mode values of “1” to “4” are reflectances Rb1, Ra2, Ra1,Ra0 of surrounding three-dimensional points b1, a2, a1, a0,respectively.

For example, when a prediction mode indicated by a prediction mode valueof “3” in the table in FIG. 73 is selected, a reflectance as anattribute value of a three-dimensional point to be encoded may beencoded as predicted value Ra1. In this case, the prediction mode valueof “3” is appended to the bitstream.

As shown in FIGS. 89 and 90 , the attribute information item may includea first attribute information item and a second attribute informationitem different from the first attribute information item. The firstattribute information item is, for example, color information. Thesecond attribute information item is, for example, reflectanceinformation. In the calculating of the predicted value, a firstpredicted value may be calculated by using the first attributeinformation item, and a second predicted value may be calculated byusing the second attribute information item.

When the attribute information item includes a plurality of componentslike color information such as a YUV color space or an RGB color space,predicted values may be calculated in different prediction modes for therespective components. For example, for the YUV space, predicted valuesusing a Y component, a U component, and a V component may be calculatedin prediction modes selected for the respective components. For example,prediction mode values may be selected in prediction mode Y forcalculating the predicted value using the Y component, prediction mode Ufor calculating the predicted value using the U component, andprediction mode V for calculating the predicted value using the Vcomponent. In this case, values in tables in FIGS. 91 to 93 describedlater may be used as the prediction mode values indicating theprediction modes of the components, and the prediction mode values maybe appended to the bitstream. The YUV color space has been describedabove, but the same applies to the RGB color space.

Predicted values including two or more components among a plurality ofcomponents of an attribute information item may be calculated in acommon prediction mode. For example, for the YUV color space, predictionmode values may be selected in prediction mode Y for calculating thepredicted value using the Y component and prediction mode UV forcalculating the predicted value using the UV components. In this case,values in tables in FIGS. 91 and 94 described later may be used as theprediction mode values indicating the prediction modes of thecomponents, and the prediction mode values may be appended to thebitstream.

FIG. 74 is a diagram showing a fifth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9. Specifically, the fifth example is an example of a casewhere an attribute information item used as a predicted value is a Ycomponent value of color information of a surrounding three-dimensionalpoint.

As shown in FIG. 74 , a predicted value calculated in prediction mode Yindicated by a prediction mode value of “0” is weighted average Yave ofY component values Yb1, Ya2, Ya1, Ya0 corresponding to points b1, a2,a1, a0, respectively. Predicted values calculated in prediction modesindicated by prediction mode values of “1” to “4” are Y component valuesYb1, Ya2, Ya1, Ya0 of surrounding three-dimensional points b1, a2, a1,a0, respectively.

For example, when prediction mode Y indicated by a prediction mode valueof “2” in the table in FIG. 74 is selected, a Y component as anattribute value of a three-dimensional point to be encoded may be usedas predicted value Ya2 and encoded. In this case, the prediction modevalue of “2” is appended to the bitstream.

FIG. 75 is a diagram showing a sixth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9. Specifically, the sixth example is an example of a casewhere an attribute information item used as a predicted value is a Ucomponent value of color information of a surrounding three-dimensionalpoint.

As shown in FIG. 75 , a predicted value calculated in prediction mode Uindicated by a prediction mode value of “0” is weighted average Uave ofU component values Ub1, Ua2, Ua1, Ua0 corresponding to points b1, a2,a1, a0, respectively. Predicted values calculated in prediction modesindicated by prediction mode values of “1” to “4” are U component valuesUb1, Ua2, Ua1, Ua0 of surrounding three-dimensional points b1, a2, a1,a0, respectively.

For example, when prediction mode U indicated by a prediction mode valueof “1” in the table in FIG. 75 is selected, a U component as anattribute value of a three-dimensional point to be encoded may beencoded as predicted value Ub1. In this case, the prediction mode valueof “1” is appended to the bitstream.

FIG. 76 is a diagram showing a seventh example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9. Specifically, the seventh example is an example of a casewhere an attribute information item used as a predicted value is a Vcomponent value of color information of a surrounding three-dimensionalpoint.

As shown in FIG. 76 , a predicted value calculated in prediction mode Vindicated by a prediction mode value of “0” is weighted average Vave ofV component values Vb1, Va2, Va1, Va0 corresponding to points b1, a2,a1, a0, respectively. Predicted values calculated in prediction modesindicated by prediction mode values of “1” to “4” are V component valuesVb1, Va2, Va1, Va0 of surrounding three-dimensional points b1, a2, a1,a0, respectively.

For example, when prediction mode V indicated by a prediction mode valueof “4” in the table in FIG. 76 is selected, a V component as anattribute value of a three-dimensional point to be encoded may be usedas predicted value Va0 and encoded. In this case, the prediction modevalue of “4” is appended to the bitstream.

FIG. 77 is a diagram showing an eighth example of a table representingpredicted values calculated in the prediction modes according toEmbodiment 9. Specifically, the eighth example is an example of a casewhere attribute information items used as predicted values are a Ucomponent value and a V component value of color information of asurrounding three-dimensional point.

As shown in FIG. 77 , a predicted value calculated in prediction mode Uindicated by a prediction mode value of “₀” includes weighted averageUave of U component values Ub1, Ub2, Ua1, Ua0 corresponding to pointsb1, a2, a1, a0, respectively and weighted average Vave of V componentvalues Vb1, Va2, Va1, Va0 corresponding to points b1, a2, a1, a0,respectively. Predicted values calculated in prediction modes indicatedby prediction mode values of “1” to “4?” include U component values andV component values of surrounding three-dimensional points b1, a2, a1,a0, respectively.

For example, when prediction mode UV indicated by a prediction modevalue of “1” in the table in FIG. 77 is selected, a U component and a Vcomponent as attribute values of a three-dimensional point to be encodedmay be used as predicted values Ub1, Vb1 and encoded. In this case, theprediction mode value of “1” is appended to the bitstream.

A prediction mode in encoding may be selected by RD optimization. Forexample, it is conceivable that cost cost(P) when certain predictionmode P is selected is calculated, and prediction mode P with minimumcost(P) is selected. Cost cost(P) may be calculated by Equation D1using, for example, prediction residual residual(P) when a predictedvalue of prediction mode P is used, bit number bit(P) required forencoding prediction mode P, and adjustment parameter value X.

cost(P)=abs(residual(P))+λ×bit(P)  (Equation D1)

-   -   where abs(x) denotes an absolute value of x.

A square value of x may be used instead of abs(x).

Using Equation D1 above allows selection of a prediction modeconsidering balance between a size of a prediction residual and a bitnumber required for encoding the prediction mode. Different adjustmentparameter values X may be set in accordance with values of aquantization scale. For example, at a small quantization scale (at ahigh bit rate), value X may be set smaller to select a prediction modewith smaller prediction residual residual(P) to increase predictionaccuracy as much as possible. At a large quantization scale (at a lowbit rate), value X may be set larger to select an appropriate predictionmode while considering bit number bit(P) required for encodingprediction mode P.

“At a small quantization scale” is, for example, when the quantizationscale is smaller than a first quantization scale. “At a largequantization scale” is, for example, when the quantization scale islarger than a second quantization scale equal to or greater than thefirst quantization scale. Values X may be set smaller at smallerquantization scales.

Prediction residual residual(P) is calculated by subtracting a predictedvalue of prediction mode P from an attribute value of athree-dimensional point to be encoded. Instead of prediction residualresidual(P) in calculating of cost, prediction residual residual(P) maybe quantized or inverse quantized and added to the predicted value toobtain a decoded value, and a difference (encoding error) from a decodedvalue when an attribute value of an original three-dimensional point andprediction mode P are used may be reflected in the cost value. Thisallows selection of a prediction mode with a small encoding error.

In binarizing and encoding a prediction mode, for example, a binarizedbit number may be used as bit number bit(P) required for encodingprediction mode P. For example, with number M of prediction modes being5, as in FIG. 78 , a prediction mode value indicating the predictionmode may be binarized by a truncated unary code in accordance withnumber M of prediction modes with a maximum value of 5. In this case, 1bit for a prediction mode value of “0”, 2 bits for a prediction modevalue of “1”, 3 bits for a prediction mode value of “2”, and 4 bits forprediction mode values of “4” and “5” are used as bit numbers bit(P)required for encoding the prediction mode values. By using the truncatedunary code, the bit number is set smaller for smaller prediction modevalues. This can reduce an encoding amount of a prediction mode valueindicating a prediction mode for calculating a predicted value that ismore likely to be selected, for example, that is more likely to minimizecost(P), such as an average value calculated as a predicted value whenthe prediction mode value is “0”, or an attribute information item of athree-dimensional point calculated as a predicted value when theprediction mode value is “1”, that is, an attribute information item ofa three-dimensional point close to the three-dimensional point to beencoded.

When a maximum value of the number of prediction modes is notdetermined, as in FIG. 79 , a prediction mode value indicating aprediction mode may be binarized by a unary code. When a probability ofappearance of each prediction mode is close, as shown in FIG. 80 , theprediction mode value indicating the prediction mode may be binarized bya fixed code to reduce an encoding amount.

As bit number bit(P) required for encoding the prediction mode valueindicating prediction mode P, binarized data of the prediction modevalue indicating prediction mode P may be arithmetic-encoded, and anencoding amount after the arithmetic-encoding may be used as a value ofbit(P). This allows calculation of cost using more accurate required bitnumber bit(P), thereby allowing selection of a more appropriateprediction mode.

FIG. 78 is a diagram showing a first example of a binarization table inbinarizing and encoding the prediction mode values according toEmbodiment 9. Specifically, the first example is an example ofbinarizing the prediction mode values with a truncated unary code withnumber M of prediction modes being 5.

FIG. 79 is a diagram showing a second example of a binarization table inbinarizing and encoding the prediction mode values according toEmbodiment 9. Specifically, the second example is an example ofbinarizing the prediction mode values with a unary code with number M ofprediction modes being 5.

FIG. 80 is a diagram showing a third example of a binarization table inbinarizing and encoding the prediction mode values according toEmbodiment 9. Specifically, the third example is an example ofbinarizing the prediction mode values with a fixed code with number M ofprediction modes being 5.

A prediction mode value indicating a prediction mode (PredMode) may bearithmetic-encoded after binarized, and appended to the bitstream. Asdescribed above, the prediction mode value may be binarized, forexample, by the truncated unary code in accordance with number M ofprediction modes. In this case, a maximum bit number after binarizationof the prediction mode value is M−1.

The binary data after binarization may be arithmetic-encoded withreference to an encoding table. In this case, for example, encodingtables may be switched for encoding for each bit of the binary data,thereby improving an encoding efficiency. Also, to reduce the number ofencoding tables, among the binary data, the beginning bit one bit may beencoded with reference to encoding table A for one bit, and each ofremaining bits may be encoded with reference to encoding table B forremaining bits. For example, in encoding binarized data “1110” with aprediction mode value of “3” in FIG. 81 , the beginning bit one bit “1”may be encoded with reference to encoding table A, and each of remainingbits “110” may be encoded with reference to encoding table B.

FIG. 81 is a diagram for describing an example of encoding binary datain the binarization table in binarizing and encoding the predictionmodes according to Embodiment 9. The binarization table in FIG. 81 is anexample of binarizing the prediction mode values with a truncated unarycode with number M of prediction modes being 5.

Thus, an encoding efficiency can be improved by switching the encodingtables in accordance with a bit position of the binary data whilereducing the number of encoding tables. Further, in encoding theremaining bits, arithmetic-encoding may be performed by switching theencoding tables for each bit, or decoding may be performed by switchingthe encoding tables in accordance with a result of thearithmetic-encoding.

In binarizing and encoding the prediction mode value with the truncatedunary code using number M of prediction modes, number M of predictionmodes used in the truncated unary code may be appended to a header andthe like of the bitstream so that the prediction mode can be specifiedfrom binary data decoded on a decoding side. Also, value MaxM that maybe a value of the number of prediction modes may be defined by standardsor the like, and value MaxM−M (M≤MaxM) may be appended to the header.Number M of prediction modes may be defined by a profile or a level ofstandards rather than appended to the stream.

It is considered that the prediction mode value binarized by thetruncated unary code is arithmetic-encoded by switching the encodingtables between one bit part and a remaining part as described above. Theprobability of appearance of 0 and 1 in each encoding table may beupdated in accordance with actually generated binary data. Also, theprobability of appearance of 0 and 1 in either of the encoding tablesmay be fixed. Thus, an update frequency of the probability of appearancemay be reduced to reduce a processing amount. For example, theprobability of appearance of the one bit part may be updated, and theprobability of appearance of the remaining bit part may be fixed.

FIG. 82 is a flowchart of an example of encoding a prediction mode valueaccording to Embodiment 9. FIG. 83 is a flowchart of an example ofdecoding a prediction mode value according to Embodiment 9.

As shown in FIG. 82 , in encoding the prediction mode value, first, theprediction mode value is binarized by a truncated unary code inaccordance with number M of prediction modes (S3401).

Then, binary data of the truncated unary code is arithmetic-encoded(S3402). Thus, the binary data is included as a prediction mode in thebitstream.

Also, as shown in FIG. 83 , in decoding the prediction mode value,first, the bitstream is arithmetic-decoded by using number M ofprediction modes to generate binary data of the truncated unary code(S3403).

Then, the prediction mode value is calculated from the binary data ofthe truncated unary code (S3404).

As a method of binarizing the prediction mode value indicating theprediction mode (PredMode), the example of binarizing with the truncatedunary code using number M of prediction modes has been described, butnot limited to this. For example, the prediction mode value may bebinarized by a truncated unary code in accordance with number L (L≤M) ofprediction modes assigned with predicted values. For example, withnumber M of prediction modes being 5, when two surroundingthree-dimensional points are available for prediction of a certainthree-dimensional point to be encoded, as shown in FIG. 84 , threeprediction modes are available and the remaining two prediction modesare not available in some cases. For example, as shown in FIG. 84 , withnumber M of prediction modes being 5, two three-dimensional points inthe vicinity of the three-dimensional point to be encoded are availablefor prediction, and predicted values are not assigned to predictionmodes indicated by prediction mode values of “3” and “4” in some cases.

In this case, as shown in FIG. 85 , the prediction mode value isbinarized by a truncated unary code in accordance with number L ofprediction modes assigned with predicted values as a maximum value. Thismay reduce the bit number after binarization as compared to when theprediction mode value is binarized by the truncated unary code inaccordance with number M of prediction modes. For example, in this case,L is 3, and the prediction mode value can be binarized by a truncatedunary code in accordance with a maximum value of 3 to reduce the bitnumber. As such, binarization by the truncated unary code in accordancewith number L of prediction modes assigned with predicted values as amaximum value may reduce the bit number after binarization of theprediction mode value.

The binary data after binarization may be arithmetic-encoded withreference to an encoding table. In this case, for example, encodingtables may be switched for encoding for each bit of the binary data,thereby improving an encoding efficiency. Also, to reduce the number ofencoding tables, among binarized data, the beginning bit one bit may beencoded with reference to encoding table A for one bit, and each ofremaining bits may be encoded with reference to encoding table B forremaining bits. For example, in encoding binarized data “11” with aprediction mode value of “2” in FIG. 85 , the beginning bit one bit “1”may be encoded with reference to encoding table A, and remaining bit “1”may be encoded with reference to encoding table B.

FIG. 85 is a diagram for describing an example of encoding binary datain the binarization table in binarizing and encoding the predictionmodes according to Embodiment 9. The binarization table in FIG. 85 is anexample of binarizing the prediction mode values with a truncated unarycode with number L of prediction modes assigned with predicted valuesbeing 3.

Thus, an encoding efficiency can be improved by switching the encodingtables in accordance with a bit position of the binary data whilereducing the number of encoding tables. Further, in encoding theremaining bits, arithmetic-encoding may be performed by switching theencoding tables for each bit, or decoding may be performed by switchingthe encoding tables in accordance with a result of thearithmetic-encoding.

In binarizing and encoding the prediction mode value with the truncatedunary code using number L of prediction modes assigned with predictedvalues, a predicted value may be assigned to a prediction mode tocalculate number L in the same manner as in encoding and the predictionmode may be decoded by using calculated number L so that the predictionmode can be specified from binary data decoded on a decoding side.

It is considered that the prediction mode value binarized by thetruncated unary code is arithmetic-encoded by switching the encodingtables between one bit part and a remaining part as described above. Theprobability of appearance of 0 and 1 in each encoding table may beupdated in accordance with actually generated binary data. Also, theprobability of appearance of 0 and 1 in either of the encoding tablesmay be fixed. Thus, an update frequency of the probability of appearancemay be reduced to reduce a processing amount. For example, theprobability of appearance of the one bit part may be updated, and theprobability of appearance of the remaining bit part may be fixed.

FIG. 86 is flowchart of another example of encoding a prediction modevalue according to Embodiment 9. FIG. 87 is a flowchart of anotherexample of decoding a prediction mode value according to Embodiment 9.

As shown in FIG. 86 , in encoding the prediction mode value, first,number L of prediction modes assigned with predicted values iscalculated (S3411).

Then, the prediction mode value is binarized by a truncated unary codein accordance with number L (S3412).

Then, binary data of the truncated unary code is arithmetic-encoded(S3413).

Also, as shown in FIG. 87 , in decoding the prediction mode value,first, number L of prediction modes assigned with predicted values iscalculated (S3414).

Then, the bitstream is arithmetic-decoded by using number L to generatebinary data of the truncated unary code (S3415).

Then, the prediction mode value is calculated from the binary data ofthe truncated unary code (S3416).

The prediction mode value needs not be appended for all attributevalues. For example, if a certain condition is satisfied, the predictionmode may be fixed so that the prediction mode value is not appended tothe bitstream, and if the certain condition is not satisfied, theprediction mode may be selected to append the prediction mode value tothe bitstream. For example, if condition A is satisfied, the predictionmode value may be fixed at “0” and a predicted value may be calculatedfrom an average value of surrounding three-dimensional points. Ifcondition A is not satisfied, one prediction mode may be selected from aplurality of prediction modes and a prediction mode value indicating theselected prediction mode may be append to the bitstream.

Certain condition A is, for example, that calculated maximum absolutedifferential value maxdiff of attribute values (a[0] to a[N−1]) of N(encoded and decoded) three-dimensional points in the vicinity of athree-dimensional point to be encoded is smaller than threshold Thfix.When the maximum absolute differential value of the attribute values ofthe surrounding three-dimensional points is smaller than thresholdThfix, it is determined that there is a small difference between theattribute values of the three-dimensional points and selecting aprediction mode causes no difference in the predicted value. Then, theprediction mode value is fixed at “0” and the prediction mode value isnot encoded, thereby allowing generation of an appropriate predictedvalue while reducing an encoding amount for encoding the predictionmode.

Threshold Thfix may be appended to the header and the like of thebitstream, and the encoder may perform encoding while changing thresholdThfix. For example, in encoding at a high bit rate, the encoder may setthreshold Thfix smaller than that at a low bit rate and append thresholdThfix to the header and increase the case of encoding by selecting aprediction mode, and thus perform encoding so that the predictionresidual is as small as possible. Also, in encoding at the low bit rate,the encoder sets threshold Thfix larger than that at the high bit rateand appends threshold Thfix to the header, and performs encoding in afixed prediction mode. As such, increasing the case of encoding in thefixed prediction mode at the low bit rate can improve an encodingefficiency while reducing a bit amount for encoding the prediction mode.Threshold Thfix may be defined by a profile or a level of standardsrather than appended to the bitstream.

The N three-dimensional points in the vicinity of the three-dimensionalpoint to be encoded, which are used for prediction, are N encoded anddecoded three-dimensional points with a distance from thethree-dimensional point to be encoded being smaller than threshold THd.A maximum value of N may be appended as NumNeighborPoint to thebitstream. The value of N needs not always match NumNeighborPoint as inthe case where the number of surrounding encoded and decodedthree-dimensional points is smaller than NumNeighborPoint.

The example has been given in which the prediction mode value is fixedat “0” if maximum absolute differential value maxdiff of the attributevalues of the three-dimensional points in the vicinity of thethree-dimensional point to be encoded, which are used for prediction, issmaller than threshold Thfix[i]. However, not limited to this, theprediction mode value may be fixed at any of “0” to “M−1”. Theprediction mode value to be fixed may be appended to the bitstream.

FIG. 88 is a flowchart of an example of a process of determining whetheror not a prediction mode value is fixed under condition A in encodingaccording to Embodiment 9. FIG. 89 is a flowchart of an example of aprocess of determining whether the prediction mode value is fixed ordecoded under condition A in decoding according to Embodiment 9.

As shown in FIG. 88 , first, the three-dimensional data encoding devicecalculates maximum absolute differential value maxdiff of attributevalues of N three-dimensional points in the vicinity of athree-dimensional point to be encoded (S3421).

Then, the three-dimensional data encoding device determines whether ornot maximum absolute differential value maxdiff is smaller thanthreshold Thfix (S3422). Threshold Thfix may be encoded and appended tothe header and the like of the stream.

When maximum absolute differential value maxdiff is smaller thanthreshold Thfix (Yes in S3422), the three-dimensional data encodingdevice sets the prediction mode value to “0” (S3423).

When maximum absolute differential value maxdiff is equal to or greaterthan threshold Thfix (No in S3422), the three-dimensional data encodingdevice selects one prediction mode from a plurality of prediction modes(S3424). A process of selecting the prediction mode will be describedlater in detail with reference to FIG. 96 .

Then, the three-dimensional data encoding device arithmetic-encodes aprediction mode value indicating the selected prediction mode (S3425).Specifically, the three-dimensional data encoding device performs stepsS3401 and S3402 described with reference to FIG. 82 to arithmetic-encodethe prediction mode value. The three-dimensional data encoding devicemay perform arithmetic-encoding by binarizing prediction mode PredModewith a truncated unary code using the number of prediction modesassigned with predicted values. Specifically, the three-dimensional dataencoding device may perform steps S3411 to S3413 described withreference to FIG. 86 to arithmetic-encode the prediction mode value.

The three-dimensional data encoding device calculates a predicted valueof the prediction mode set in step S3423 or the prediction mode selectedin step S3425 to output the calculated predicted value (S3426). Whenusing the prediction mode value set in step S3423, the three-dimensionaldata encoding device calculates an average of attribute values of Nsurrounding three-dimensional points as a predicted value of aprediction mode indicated by the prediction mode value of “0”.

Also, as shown in FIG. 89 , first, the three-dimensional data decodingdevice calculates maximum absolute differential value maxdiff ofattribute values of N three-dimensional points in the vicinity of athree-dimensional point to be decoded (S3431). Maximum absolutedifferential value maxdiff may be calculated as shown in FIG. 90 .Maximum absolute differential value maxdiff is, for example, a maximumvalue of a plurality of calculated absolute values of differencesbetween all possible pairs when any two of the N surroundingthree-dimensional points are paired.

Then, the three-dimensional data decoding device determines whether ornot maximum absolute differential value maxdiff is smaller thanthreshold Thfix (S3432). Threshold Thfix may be set by decoding theheader and the like of the stream.

When maximum absolute differential value maxdiff is smaller thanthreshold Thfix (Yes in S3432), the three-dimensional data decodingdevice sets the prediction mode value to “0” (S3433).

When maximum absolute differential value maxdiff is equal to or greaterthan threshold Thfix (No in S3432), the three-dimensional data decodingdevice decodes the prediction mode value from the bitstream (S3434).

The three-dimensional data decoding device calculates a predicted valueof a prediction mode indicated by the prediction mode value set in stepS3433 or the prediction mode value decoded in step S3434 to output thecalculated predicted value (S3435). When using the prediction mode valueset in step S3433, the three-dimensional data decoding device calculatesan average of attribute values of N surrounding three-dimensional pointsas a predicted value of a prediction mode indicated by the predictionmode value of “0”.

FIG. 91 is a diagram showing an example of a syntax according toEmbodiment 9. NumLoD, NumNeighborPoint[i], NumPredMode[i], Thfix[i], andNumOfPoint[i] in the syntax in FIG. 91 will be sequentially described.

NumLoD indicates the number of LoDs.

NumNeighborPoint[i] indicates an upper limit value of the number ofsurrounding points used for generating a predicted value of athree-dimensional point belonging to level i. When number M ofsurrounding points is smaller than NumNeighborPoint[i](M<NumNeighborPoint[i]), the predicted value may be calculated by usingnumber M of surrounding points. When a value of NumNeighborPoint[i]needs not be divided among LoDs, one NumNeighborPoint may be appended tothe header.

NumPredMode[i] is a total number (M) of prediction modes used forpredicting an attribute value of level i. Possible value MaxM of thenumber of prediction modes may be defined by standards or the like, andvalue MaxM-M (0<M≤MaxM) may be appended to the header as NumPredMode[i]and binarized by a truncated unary code in accordance with maximum valueMaxM−1 and encoded. Number of prediction modes NumPredMode[i] may bedefined by a profile or a level of standards rather than appended to thestream. The number of prediction modes may be defined byNumNeighborPoint[i]+NumPredMode[i]. When the value of NumPredMode[i]needs not be divided among LoDs, one NumPredMode may be appended to theheader.

Thfix[i] indicates a threshold of a maximum absolute differential valuefor determining whether or not a prediction mode of level i is fixed. Ifthe maximum absolute differential value of attribute values ofsurrounding three-dimensional points used for prediction is smaller thanThfix[i], the prediction mode is fixed at 0. Thfix[i] may be defined bya profile or a level of standards rather than appended to the stream.When the value of Thfix[i] needs not be divided among LoDs, one Thfixmay be appended to the header.

NumOfPoint[i] indicates the number of three-dimensional points belongingto level i. When total number AllNumOfPoint of three-dimensional pointsis appended to another header, NumOfPoint[NumLoD−1] (the number ofthree-dimensional points belonging to the lowest level) may becalculated by the following equation:

[Math. 5]

AllNumOfPoint−Σ_(j=0) ^(NumLoD-2)NumOfPoint[j]  (Equation D2)

rather than appended to the header. This can reduce an encoding amountof the header.

As a setting example of NumPredMode[i], the value of NumPredMode[i] maybe set larger for a higher level at which prediction is more difficultdue to a longer distance between three-dimensional points belonging tothe level, thereby increasing the number of selectable prediction modes.Also, the value NumPredMode[i] may be set smaller for a lower level atwhich prediction is easy, thereby reducing a bit amount required forencoding the prediction mode. Such setting can increase the number ofselectable prediction modes to reduce a prediction residual at the upperlevel, and reduce an encoding amount of a prediction mode at the lowerlevel, thereby improving an encoding efficiency.

As a setting example of Thfix[i], the value of Thfix[i] may be setsmaller for a higher level at which prediction is difficult due to along distance from the three-dimensional point belonging to LoD, therebyincreasing the case of selecting the prediction mode. Also, the value ofThfix[i] may be set larger value for a lower level at which predictionis easy, and the prediction mode may be fixed to reduce a bit amountrequired for encoding the prediction mode. Such setting can increase thecase of selecting the prediction mode to reduce a prediction residual atthe upper level, and fix the prediction mode to reduce an encodingamount of the prediction mode at the lower level, thereby improving anencoding efficiency.

NumLoD, NumNeighborPoint[i], NumPredMode[i], Thfix[i], or NumOfPoint[i]described above may be entropy encoded and appended to the header. Forexample, the values may be binarized and arithmetic-encoded. The valuesmay be encoded at a fixed length to reduce a processing amount.

FIG. 92 is a diagram showing an example of a syntax according toEmbodiment 9. PredMode, n-bit code, and remaining code in the syntax inFIG. 92 will be sequentially described.

PredMode indicates a prediction mode for encoding and decoding anattribute value of a j-th three-dimensional point in level i. PredModeis “0” to “M−1” (M is a total number of prediction modes). When PredModeis not in the bitstream (maxdiff Thfix[i] && NumPredMode[i]>1 as acondition is not satisfied), the value of PredMode may be estimated as0. The value of PredMode may be estimated as any of “0” to “M−1”, notlimited to “0”. An estimated value when PredMode is not in the bitstreammay be separately appended to the header and the like. PredMode may bebinarized by a truncated unary code in accordance with the number ofprediction modes assigned with predicted values and arithmetic-encoded.

The n-bit code indicates encoded data of a prediction residual of avalue of an attribute information item. A bit length of the n-bit codedepends on a value of R_TH[i]. The bit length of the n-bit code is, forexample, 6 bits when the value of R_TH[i] is 63, and 8 bits when thevalue of R_TH[i] is 255.

The remaining code indicates encoded data encoded by Exponential-Golombamong encoded data of the prediction residual of the attributeinformation item. The remaining code is decoded when the n-bit code isequal to R_TH[i], and the value of the n-bit code is added to the valueof the remaining code to decode the prediction residual. When the n-bitcode is not equal to R_TH[i], the remaining code needs not be decoded.

Now, a flow of a process of the three-dimensional data encoding devicewill be described. FIG. 93 is a flowchart of a three-dimensional dataencoding process by the three-dimensional data encoding device accordingto Embodiment 9.

First, the three-dimensional data encoding device encodes geometryinformation (geometry) (S3441). For example, three-dimensional dataencoding is encoding by using an octree representation.

When a position of a three-dimensional point is changed by quantizationor the like after encoding of geometry information, thethree-dimensional data encoding device reassigns an attributeinformation item of an original three-dimensional point to thethree-dimensional point after change (S3442). For example, thethree-dimensional data encoding device performs reassignment byinterpolating a value of the attribute information item in accordancewith a change amount of the position. For example, the three-dimensionaldata encoding device detects N three-dimensional points before changeclose to the three-dimensional position after change, and performsweighted averaging of values of attribute information items of the Nthree-dimensional points. For example, the three-dimensional dataencoding device determines, in the weighted averaging, a weight based ona distance from the three-dimensional position after change to each ofthe N three-dimensional points. Then, the three-dimensional dataencoding device sets the value obtained by the weighted averaging to avalue of an attribute information item of the three-dimensional pointafter change. When two or more three-dimensional points are changed tothe same three-dimensional position by quantization or the like, thethree-dimensional data encoding device may assign an average value ofattribute information items of two or more three-dimensional pointsbefore change as a value of an attribute information item of athree-dimensional point after change.

Then, the three-dimensional data encoding device encodes the attributeinformation item (Attribute) after reassignment (S3443). For example,when encoding multiple types of attribute information items, thethree-dimensional data encoding device may sequentially encode themultiple types of attribute information items. For example, whenencoding a color and a reflectance as attribute information items, thethree-dimensional data encoding device may generate a bitstream appendedwith a color encoding result followed by a reflectance encoding result.The plurality of encoding results of the attribute information items maybe appended to the bitstream in any order, not limited to this.

The three-dimensional data encoding device may append informationindicating an encoded data start location of each attribute informationitem in the bitstream to the header and the like. Thus, thethree-dimensional data decoding device can selectively decode anattribute information item that requires decoding, thereby omitting adecoding process of an attribute information item that does not requiredecoding. This can reduce a processing amount of the three-dimensionaldata decoding device. The three-dimensional data encoding device mayconcurrently encode multiple types of attribute information items, andcombine encoding results into one bitstream. Thus, the three-dimensionaldata encoding device can quickly encode the multiple types of attributeinformation items.

FIG. 94 is a flowchart of an attribute information item encoding process(S3443) according to Embodiment 9. First, the three-dimensional dataencoding device sets LoD (S3451). Specifically, the three-dimensionaldata encoding device assigns each three-dimensional point to any of aplurality of LoDs.

Then, the three-dimensional data encoding device starts a loop for eachLoD (S3452). Specifically, the three-dimensional data encoding devicerepeats processes in steps S3453 to S3461 for each LoD.

Then, the three-dimensional data encoding device starts a loop for eachthree-dimensional point (S3453). Specifically, the three-dimensionaldata encoding device repeats processes in steps S3454 to S3460 for eachthree-dimensional point.

First, the three-dimensional data encoding device searches a pluralityof surrounding points that are three-dimensional points in the vicinityof a target three-dimensional point to be processed and used forcalculating a predicted value of the target three-dimensional point(S3454).

Then, the three-dimensional data encoding device calculates predictedvalue P of the target three-dimensional point (S3455). A specificexample of a calculation process of predicted value P will be describedlater with reference to FIG. 95 .

Then, the three-dimensional data encoding device calculates, as aprediction residual, a difference between an attribute information itemof the target three-dimensional point and the predicted value (S3456).

Then, the three-dimensional data encoding device quantizes theprediction residual to calculate a quantized value (S3457).

Then, the three-dimensional data encoding device arithmetic-encodes thequantized value (S3458).

The three-dimensional data encoding device inverse quantizes thequantized value to calculate an inverse quantized value (S3459).

Then, the three-dimensional data encoding device adds the predictedvalue to the inverse quantized value to generate a decoded value(S3460).

Then, the three-dimensional data encoding device finishes the loop foreach three-dimensional point (S3461).

The three-dimensional data encoding device also finishes the loop foreach LoD (S3462).

FIG. 95 is a flowchart of a predicted value calculation process (S3455)by the three-dimensional data encoding device according to Embodiment 9.

First, the three-dimensional data encoding device calculates a weightedaverage of attribute values of the N three-dimensional points in thevicinity of the target three-dimensional point to be processed, whichcan be used for predicting a predicted value of the targetthree-dimensional point, and assigns the calculated weighted average toa prediction mode indicated by a prediction mode value of “0” (S3420).

Then, the three-dimensional data encoding device performs steps S3421 toS3426 described with reference to FIG. 88 to output a predicted value ofthe target three-dimensional point.

FIG. 96 is a flowchart of a prediction mode selection process (S3424)according to Embodiment 9.

First, the three-dimensional data encoding device assigns attributeinformation items of the N three-dimensional points in the vicinity ofthe target three-dimensional point to prediction mode values of “1” to“N” in one increment sequentially from a three-dimensional point closerto the target three-dimensional point (S3427). The prediction modevalues of “0” to “N” are assigned, and thus a total of N+1 predictionmodes are generated. When N+1 is larger than maximum number M ofprediction modes (NumPredMode) appended to the bitstream, thethree-dimensional data encoding device may generate up to M predictionmodes.

Then, the three-dimensional data encoding device calculates cost of eachprediction mode, and selects a prediction mode with minimum cost(S3428).

FIG. 97 is a flowchart of a prediction mode selection process (S3428)that minimizes cost according to Embodiment 9.

First, the three-dimensional data encoding device sets i=0 and mincost=∞as initial values (S3471). The set initial values of i and mincost arestored in a memory of the three-dimensional data encoding device.

Then, the three-dimensional data encoding device calculates cost cost[i]of i-th prediction mode PredMode[i] using, for example, Equation D1(S3472).

Then, the three-dimensional data encoding device determines whether ornot calculated cost cost[i] is smaller than mincost stored in the memory(S3473).

Then, when calculated cost cost[i] is smaller than mincost stored in thememory (Yes in S3473), the three-dimensional data encoding device setsmincost=cost[i], sets the prediction mode to predmode[i] (S3474), andgoes to step S3475. Specifically, the value of mincost stored in thememory is updated to the value of cost[i], and predmode[i] is stored asa prediction mode in the memory.

When, calculated cost cost[i] is equal to or greater than mincost storedin the memory (No in S3473), the three-dimensional data encoding devicegoes to step S3475.

Then, the three-dimensional data encoding device increments the value ofi by one (S3475).

Then, the three-dimensional data encoding device determines whether ornot i is smaller than the number of prediction modes (S3476).

When i is smaller than the number of prediction modes (Yes in S3476),the three-dimensional data encoding device returns to step S3472, andwhen not (No in S3476), the three-dimensional data encoding devicefinishes the process of selecting the prediction mode that minimizescost.

Now, a flow of a process of the three-dimensional data decoding devicewill be described. FIG. 98 is a flowchart of a three-dimensional datadecoding process by the three-dimensional data decoding device accordingto Embodiment 9. First, the three-dimensional data decoding devicedecodes the geometry information (geometry) from the bitstream (S3444).For example, the three-dimensional data decoding device performsdecoding by using an octree representation.

Then, the three-dimensional data decoding device decodes an attributeinformation item (Attribute) from the bitstream (S3445). For example,when decoding multiple types of attribute information items, thethree-dimensional data decoding device may sequentially decode themultiple types of attribute information items. For example, whendecoding a color and a reflectance as attribute information items, thethree-dimensional data decoding device decodes a color encoding resultand a reflectance encoding result in the order of appending to thebitstream. For example, when the bitstream is appended with the colorencoding result followed by the reflectance encoding result, thethree-dimensional data decoding device decodes the color encoding resultand then decodes the reflectance encoding result. The three-dimensionaldata decoding device may decode encoding results of the attributeinformation items appended to the bitstream in any order.

The three-dimensional data decoding device may obtain informationindicating an encoded data start location of each attribute informationitem in the bitstream by decoding the header and the like. Thus, thethree-dimensional data decoding device can selectively decode anattribute information item that requires decoding, thereby omitting adecoding process of an attribute information item that does not requiredecoding. This can reduce a processing amount of the three-dimensionaldata decoding device. The three-dimensional data decoding device mayconcurrently decode multiple types of attribute information items, andcombine decoding results into one three-dimensional point cloud. Thus,the three-dimensional data decoding device can quickly decode themultiple types of attribute information items.

FIG. 99 is a flowchart of an attribute information item decoding process(S3445) according to Embodiment 9. First, the three-dimensional datadecoding device sets LoD (S3481). Specifically, the three-dimensionaldata decoding device assigns each of a plurality of three-dimensionalpoints having decoded geometry information to any of a plurality ofLoDs. For example, this assignment method is the same as that used bythe three-dimensional data encoding device.

Then, the three-dimensional data decoding device starts a loop for eachLoD (S3482). Specifically, the three-dimensional data decoding devicerepeats processes in steps S3483 to S3489 for each LoD.

Then, the three-dimensional data decoding device starts a loop for eachthree-dimensional point (S3483). Specifically, the three-dimensionaldata decoding device repeats processes in steps S3484 to S3488 for eachthree-dimensional point.

First, the three-dimensional data decoding device searches a pluralityof surrounding points that are three-dimensional points in the vicinityof a target three-dimensional point to be processed and used forcalculating a predicted value of the target three-dimensional point(S3484). This process is the same as that by the three-dimensional dataencoding device.

Then, the three-dimensional data decoding device calculates predictedvalue P of the target three-dimensional point (S3485).

Then, the three-dimensional data decoding device arithmetic-decodes aquantized value from the bitstream (S3486).

The three-dimensional data decoding device also inverse quantizes thedecoded quantized value to calculate an inverse quantized value (S3487).

Then, the three-dimensional data decoding device adds the predictedvalue to the inverse quantized value to generate a decoded value(S3488).

Then, the three-dimensional data decoding device finishes the loop foreach three-dimensional point (S3489).

The three-dimensional data decoding device also finishes the loop foreach LoD (S3490).

FIG. 100 is a flowchart of a predicted value calculation process (S3485)by the three-dimensional data decoding device according to Embodiment 9.

First, the three-dimensional data decoding device calculates a weightedaverage of attribute values of the N three-dimensional points in thevicinity of the target three-dimensional point to be processed, whichcan be used for predicting a predicted value of the targetthree-dimensional point, and assigns the calculated weighted average toa prediction mode indicated by a prediction mode value of “0” (S3430).

Then, the three-dimensional data decoding device performs steps S3431 toS3435 described with reference to FIG. 89 to output a predicted value ofthe target three-dimensional point.

Instead of performing step S3430, after it is determined Yes in stepS3432 or when the prediction mode value decoded in step S3434 is “0”,the weighted average of the attribute values of the N three-dimensionalpoints in the vicinity of the target three-dimensional point to beprocessed, which can be used for predicting a predicted value of thetarget three-dimensional point, may be calculated as a predicted value.This eliminates the need to calculate the average value in predictionmodes other than the prediction mode indicated by the prediction modevalue of “0”, thereby reducing a processing amount.

FIG. 101 is a flowchart of a prediction mode decoding process (S3434)according to Embodiment 9.

First, the three-dimensional data decoding device assigns attributeinformation items of the N three-dimensional points in the vicinity ofthe target three-dimensional point to prediction mode values of “1” to“N” in one increment sequentially from a three-dimensional point closerto the target three-dimensional point (S3491). The prediction modevalues of “0” to “N” are assigned, and thus a total of N+1 predictionmodes are generated. When N+1 is larger than maximum number M ofprediction modes (NumPredMode) appended to the bitstream, thethree-dimensional data decoding device may generate up to M predictionmodes.

Then, the three-dimensional data decoding device arithmetic-decodes theprediction mode by using the number of prediction modes (S3492).Specifically, the three-dimensional data decoding device may performsteps S3403 and S3404 described with reference to FIG. 83 toarithmetic-decode the prediction mode. The three-dimensional datadecoding device may also perform steps S3414 to S3416 described withreference to FIG. 87 to arithmetic-decode the prediction mode.

FIG. 102 is a block diagram showing a configuration of attributeinformation encoder 3100 included in the three-dimensional data encodingdevice according to Embodiment 9. FIG. 102 shows an attributeinformation encoder in detail among a geometry information encoder, anattribute information item reassigner, and the attribute informationencoder included in the three-dimensional data encoding device.

Attribute information encoder 3400 includes LoD generator 3401,surrounding searcher 3402, predictor 3403, prediction residualcalculator 3404, quantizer 3405, arithmetic encoder 3406, inversequantizer 3407, decoded value generator 3408, and memory 3409.

LoD generator 3401 generates LoD by using geometry information(geometry) of a three-dimensional point.

Surrounding searcher 3402 searches a neighboring three-dimensional pointadjacent to each three-dimensional point by using an LoD generationresult by LoD generator 3401 and distance information indicating adistance between three-dimensional points.

Predictor 3403 generates a predicted value of an attribute informationitem of a target three-dimensional point to be encoded. Specifically,predictor 3403 assigns predicted values to prediction modes indicated byprediction mode values of “0” to “M−1”, and selects a prediction mode.Predictor 3403 outputs the selected prediction mode, specifically, theprediction mode value indicating the prediction mode to the arithmeticencoder. Predictor 3403 performs, for example, the process in stepS3455.

Prediction residual calculator 3404 calculates (generates) a predictionresidual of the predicted value of the attribute information itemgenerated by predictor 3403. Prediction residual calculator 3404performs the process in step S3456.

Quantizer 3405 quantizes the prediction residual of the attributeinformation item calculated by prediction residual calculator 3404.

Arithmetic encoder 3106 arithmetic-encodes the prediction residualquantized by quantizer 3105. Arithmetic encoder 3406 outputs a bitstreamincluding the arithmetic-encoded prediction residual, for example, tothe three-dimensional data decoding device.

The prediction residual may be binarized, for example, by quantizer 3405before arithmetic-encoded by arithmetic encoder 3406. Arithmetic encoder3406 may generate and encode various header information items (headerinformation). Arithmetic encoder 3406 may also obtain a prediction modeused for encoding from Prediction block, and arithmetic-encode andappend the prediction mode to the bitstream.

Inverse quantizer 3407 inverse quantizes the prediction residualquantized by quantizer 3405. Inverse quantizer 3407 performs the processin step S3459.

Decoded value generator 3408 adds the predicted value of the attributeinformation item generated by predictor 3403 to the prediction residualinverse quantized by inverse quantizer 3407 to generate a decoded value.

Memory 3409 stores a decoded value of an attribute information item ofeach three-dimensional point decoded by decoded value generator 3408.For example, when generating a predicted value of a three-dimensionalpoint having been not yet encoded, predictor 3403 generates a predictedvalue by using the decoded value of the attribute information item ofeach three-dimensional point stored in memory 3409.

FIG. 103 is a block diagram of a configuration of attribute informationdecoder 3410 included in the three-dimensional data decoding deviceaccording to Embodiment 9. FIG. 103 shows an attribute informationdecoder in detail among a geometry information decoder and the attributeinformation decoder included in the three-dimensional data decodingdevice.

Attribute information decoder 3410 includes LoD generator 3411,surrounding searcher 3412, predictor 3413, arithmetic decoder 3414,inverse quantizer 3415, decoded value generator 3416, and memory 3417.

LoD generator 3411 generates LoD by using geometry information (geometryinformation) of a three-dimensional point decoded by a geometryinformation decoder (not shown).

Surrounding searcher 3412 searches a neighboring three-dimensional pointadjacent to each three-dimensional point by using an LoD generationresult by LoD generator 3411 and distance information indicating adistance between three-dimensional points.

Predictor 3413 generates a predicted value of an attribute informationitem of a target three-dimensional point to be decoded. Predictor 3413performs, for example, the process in step S3485.

Arithmetic decoder 3414 arithmetic-decodes the prediction residual inthe bitstream obtained by attribute information encoder 3400. Arithmeticdecoder 3414 may decode various header information items. Arithmeticdecoder 3414 may also output an arithmetic-decoded prediction mode topredictor 3413. In this case, predictor 3413 may calculate a predictedvalue by using the prediction mode obtained by arithmetic decoding byarithmetic decoder 3414.

Inverse quantizer 3415 inverse quantizes the prediction residualarithmetic-decoded by arithmetic decoder 3414.

Decoded value generator 3416 adds the predicted value generated bypredictor 3413 and the prediction residual inverse quantized by inversequantizer 3415 to generate a decoded value. Decoded value generator 3416outputs decoded attribute information data to any other device.

Memory 3417 stores a decoded value of an attribute information item ofeach three-dimensional point decoded by decoded value generator 3416.For example, when generating a predicted value of a three-dimensionalpoint having been not yet decoded, predictor 3413 generates a predictedvalue by using the decoded value of the attribute information item ofeach three-dimensional point stored in memory 3417.

Next, configurations of the three-dimensional data encoding device andthe three-dimensional data decoding device according to this embodimentwill be described. FIG. 104 is a block diagram showing the configurationof three-dimensional data encoding device 3420 according to Embodiment9. Three-dimensional data encoding device 3420 includes prediction modeselector 3421, predicted value calculator 3422, prediction residualcalculator 3423, and encoder 3424. Prediction mode selector 3421 selectsone prediction mode from two or more prediction modes in accordance withattribute information items of one or more second three-dimensionalpoints in the vicinity of a first three-dimensional point, the two ormore prediction modes each being used to calculate a predicted value ofan attribute information item of the first three-dimensional point.Predicted value calculator 3422 calculates the predicted value of theprediction mode selected by prediction mode selector 3421. Predictionresidual calculator 3423 calculates, as a prediction residual, adifference between the attribute information item of the firstthree-dimensional point and the predicted value calculated by predictedvalue calculator 3422. Encoder 3424 generates a bitstream including theselected prediction mode and the calculated prediction residual.

FIG. 105 is a block diagram showing the configuration ofthree-dimensional data decoding device 3430 according to Embodiment 9.Three-dimensional data decoding device 3430 includes obtainer 3431,predicted value calculator 3432, and decoder 3433. Obtainer 3431 obtainsa bitstream to obtain a prediction mode and a prediction residual.Predicted value calculator 3432 calculates a predicted value of theobtained prediction mode. Decoder 3433 adds the calculated predictedvalue to the obtained prediction residual to calculate an attributeinformation item of the first three-dimensional point.

The three-dimensional data encoding device according to this embodimentperforms processes shown in FIG. 106 .

The three-dimensional data encoding device selects one prediction modefrom two or more prediction modes in accordance with attributeinformation items of one or more second three-dimensional points in thevicinity of the first three-dimensional point, the two or moreprediction modes each being used to calculate a predicted value of anattribute information item of the first three-dimensional point (S3421a). Then, the three-dimensional data encoding device calculates thepredicted value of the selected prediction mode (S3422 a). Then, thethree-dimensional data encoding device calculates, as a predictionresidual, a difference between the attribute information item of thefirst three-dimensional point and the calculated predicted value (S3423a). Then, the three-dimensional data encoding device generates abitstream including the selected prediction mode and the calculatedprediction residual (S3424 a).

Thus, the three-dimensional data encoding device can encode an attributeinformation item by using a predicted value of one prediction mode amongtwo or more prediction modes, thereby improving an encoding efficiencyof an attribute information item.

In the calculating of the predicted value (S3422 a), thethree-dimensional data encoding device calculates, as the predictedvalue, an average of the attribute information items of the one or moresecond three-dimensional points in a first prediction mode among the twoor more prediction modes, and calculates, as the predicted value, anattribute information item of the one or more second three-dimensionalpoints in a second prediction mode among the two or more predictionmodes.

A first prediction mode value indicating the first prediction mode issmaller than a second prediction mode value indicating the secondprediction mode. The bitstream includes, as the prediction mode, aprediction mode value indicating the prediction mode selected.

In the calculating of the predicted value (S3422 a), two or moreaverages or two or more attribute information items are calculated asthe predicted value of the prediction mode.

The attribute information item is color information indicating a colorof a corresponding three-dimensional point, and the two or more averagesor the two or more attribute information items indicate two or morecomponent values defining a color space.

In the calculating of the predicted value (S3422 a), thethree-dimensional data encoding device calculates, as the predictedvalue, an attribute information item of one second three-dimensionalpoint in a third prediction mode among the two or more prediction modes,and calculates, as the predicted value, an attribute information item ofanother second three-dimensional point farther from the firstthree-dimensional point than the one second three-dimensional point in afourth prediction mode among the two or more prediction modes. A thirdprediction mode value indicating the third prediction mode is smallerthan a fourth prediction mode value indicating the fourth predictionmode. The bitstream includes, as the prediction mode, a prediction modevalue indicating the prediction mode selected. Thus, a prediction modevalue indicating a prediction mode for calculating a predicted valuethat is more likely to be selected is set smaller, thereby reducing anencoding amount.

The attribute information item includes a first attribute informationitem and a second attribute information item different from the firstattribute information item. In the calculating of the predicted value(S3422 a), the three-dimensional data encoding device calculates a firstpredicted value by using the first attribute information item, andcalculates a second predicted value by using the second attributeinformation item.

The three-dimensional data encoding device further binarizes theprediction mode value indicating the prediction mode with a truncatedunary code in accordance with the number of prediction modes to generatebinary data. The bitstream includes the binary data as the predictionmode. This can reduce an encoding amount of a prediction mode valueindicating a prediction mode for calculating a predicted value that ismore likely to be selected.

In the selecting of the prediction mode (S3421 a), when a maximumabsolute differential value of the attribute information items of theone or more second three-dimensional points is smaller than apredetermined threshold, the three-dimensional data encoding deviceselects a prediction mode for calculating, as the predicted value, anaverage of the attribute information items of the one or more secondthree-dimensional points, and when the maximum absolute differentialvalue is equal to or greater than the predetermined threshold, thethree-dimensional data encoding device selects one prediction mode fromthe two or more prediction modes. This can reduce a processing amountwhen the maximum absolute differential value is smaller than thepredetermined threshold.

The three-dimensional data decoding device according to this embodimentperforms processes shown in FIG. 107 .

The three-dimensional data decoding device obtains a bitstream to obtaina prediction mode and a prediction residual (S3431 a). Then, thethree-dimensional data decoding device calculates a predicted value ofthe obtained prediction mode (S3432 a). Then, the three-dimensional dataencoding device adds the calculated predicted value to the obtainedprediction residual to calculate an attribute information item of thefirst three-dimensional point (S3433 a).

This can reduce a processing amount when the maximum absolutedifferential value is smaller than the predetermined threshold.

In the calculating of the predicted value (S3432 a), thethree-dimensional data decoding device calculates, as the predictedvalue, an average of the attribute information items of one or moresecond three-dimensional points in the vicinity of the firstthree-dimensional point in the first prediction mode among the two ormore prediction modes, and calculates, as the predicted value, anattribute information item of the one or more second three-dimensionalpoints in the second prediction mode among the two or more predictionmodes.

A first prediction mode value indicating the first prediction mode issmaller than a second prediction mode value indicating the secondprediction mode. The bitstream includes, as the prediction mode, aprediction mode value indicating the prediction mode selected.

In the calculating of the predicted value (S3432 a), thethree-dimensional data decoding device calculates two or more averagesor two or more attribute information items as the predicted value of theprediction mode.

The attribute information item is color information indicating a colorof a corresponding three-dimensional point, and the two or more averagesor the two or more attribute information items indicate two or morecomponent values defining a color space.

In the calculating of the predicted value (S3432 a), an attributeinformation item of one second three-dimensional point among one or moresecond three-dimensional points in the vicinity of the firstthree-dimensional point is calculated as the predicted value in a thirdprediction mode among the two or more prediction modes, and an attributeinformation item of another second three-dimensional point farther fromthe first three-dimensional point than the one second three-dimensionalpoint is calculated as the predicted value in a fourth prediction modeamong the two or more prediction modes. A third prediction mode valueindicating the third prediction mode is smaller than a fourth predictionmode value indicating the fourth prediction mode. The bitstreamincludes, as the prediction mode, a prediction mode value indicating theprediction mode selected. Thus, a prediction mode value indicating aprediction mode for calculating a predicted value that is more likely tobe selected is set smaller, thereby reducing time for decoding anattribute information item.

The attribute information item includes a first attribute informationitem and a second attribute information item different from the firstattribute information item. In the calculating of the predicted value(S3432 a), a first predicted value is calculated by using the firstattribute information item, and a second predicted value is calculatedby using the second attribute information item.

In the obtaining of the prediction mode (S3431 a), the three-dimensionaldata decoding device obtains, as the prediction mode, binary databinarized by a truncated unary code in accordance with the number ofprediction modes.

In the calculating of the predicted value (S3432 a), when a maximumabsolute differential value of the attribute information item of the oneor more second three-dimensional points in the vicinity of the firstthree-dimensional point is smaller than a predetermined threshold, anaverage of the attribute information items of the one or more secondthree-dimensional points is calculated as the predicted value, and whenthe maximum absolute differential value is equal to or greater than thepredetermined threshold, a predicted value of one prediction mode amongthe two or more prediction modes is calculated as the predicted value.

This can reduce a processing amount when the maximum absolutedifferential value is smaller than the predetermined threshold.

Embodiment 10

When a three-dimensional data encoding device arithmetically encodesprediction mode values each indicating a prediction mode (PredMode) bybinarizing the prediction mode values using truncated unary codingaccording to the value of M, where M denotes the total number ofprediction modes, there is a possibility of generating a prediction modeto which a predicted value is not assigned due to the number of encodedand decoded three-dimensional points which can be used for predictionand are in the vicinity of a three-dimensional point to be encoded. Thethree-dimensional data encoding device is also referred to as anencoder. A three-dimensional data decoding device is also referred to asa decoder. The embodiment will be described with reference to FIG. 108through FIG. 110 .

FIG. 108 is a diagram according to Embodiment 10 which illustrates anexample of a table indicating predicted values calculated according torespective prediction modes by a three-dimensional data encoding device.FIG. 109 is a diagram according to Embodiment 10 which illustrates anexample of a binarization table indicating a case in which predictionmode values are encoded by binarization. FIG. 110 is a diagram accordingto Embodiment 10 which illustrates an example of a table indicatingpredicted values calculated according to the respective prediction modesby the three-dimensional data decoding device.

For example, FIG. 108 illustrates an example in which, when the totalnumber of prediction modes denoted by M is five and the number ofthree-dimensional points denoted by N is two, as predicted valuescalculated according to prediction modes, (i) an average value isassigned to prediction mode 0, (ii) attribute values of Nthree-dimensional points, where N denotes two, are respectively assignedto prediction mode 1 and prediction mode 2, and (iii) a predicted valueis not assigned to prediction mode 3 and prediction mode 4. Nthree-dimensional points are in the vicinity of a three-dimensionalpoint to be processed and can be used for prediction. In such a case,“not available” indicating an undefined value may be assigned as apredicted value calculated according to prediction mode 3 and predictionmode 4 to both of which a predicted value is not assigned. Since thethree-dimensional data encoding device performs encoding according tothe value of M, where M denotes the total number of prediction modes andis five in this case, the three-dimensional data encoding device iscapable of selecting any one of the five prediction modes. Accordingly,the three-dimensional data encoding device is capable of erroneouslyselecting prediction mode 3 or prediction mode 4, and appending, to abitstream, a prediction mode value indicating prediction mode 3 orprediction mode 4 which is erroneously selected. As a consequence, thethree-dimensional data decoding device is capable of obtaining abitstream including the prediction mode value that is erroneouslyappended, and decoding the prediction mode value that is appended to theobtained bitstream. For this reason, the three-dimensional data decodingdevice may not be able to correctly decode the bitstream. Hereinafter,the above will be described.

For example, as illustrated in FIG. 109 , although the three-dimensionaldata encoding device erroneously selects prediction mode 3 to which apredicted value is not assigned, the three-dimensional data encodingdevice can arithmetically encode “1110” that is binary data of “3” whichis a prediction mode value indicating prediction mode 3. Thearithmetically encoded binary data is appended to the bitstream. Itshould be noted that an example illustrated in FIG. 109 indicates binarydata obtained as the result of binarizing prediction mode values usingtruncated unary coding according to M, where M denotes the total numberof prediction modes and is five. It should be noted that a method ofbinarizing the prediction mode values need not use the truncated unarycoding. The prediction mode values may be binarized using unary codingor fixed coding.

Then, as illustrated in FIG. 110 , when the three-dimensional datadecoding device obtains the bitstream generated by the three-dimensionalencoding device, the three-dimensional data decoding device can obtain,by decoding data included in the bitstream, “1110”, that is the binarydata of a prediction mode value and “3” which is the prediction modevalue. Then, the three-dimensional data decoding device decodes anattribute value of a three-dimensional point to be decoded bycalculating a predicted value according to prediction mode 3.

However, prediction mode 3 obtained from the bitstream is a predictionmode erroneously appended, and is the prediction mode to which anundefined value is appended. For this reason, the three-dimensional datadecoding device may calculate a value different from the predicted valuecalculated according to prediction mode 3 by the three-dimensional dataencoding device, when calculating a predicted value according toprediction mode 3. That is, unless a predicted value is calculated usinga calculation method common to an encoder and a decoder, a discrepancymay occur between a predicted value calculated by the encoder and apredicted value calculated by the decoder. If the discrepancy occurs,the discrepancy affects subsequent prediction performed by the decoder,and the three-dimensional data decoding device may not be able tocorrectly decode the bitstream.

Accordingly, by predetermining a calculation method which is common tothe encoder and the decoder and in which a predetermined fixed value isassigned to a prediction mode to which a predicted value is notassigned, the decoder can decode a prediction mode from a bitstream, canobtain a decoded value using the same initial value used by the encoder,and can correctly decode the bitstream, even if the encoder erroneouslygenerates a prediction residual according to a prediction mode to whicha predicted value is not assigned (prediction mode to which an initialvalue is assigned) and appends the prediction mode to the bitstream.

FIG. 111 is a diagram according to Embodiment 10 which illustrates anexample of a table indicating predicted values calculated according tothe respective prediction modes by the three-dimensional data encodingdevice. FIG. 112 is a diagram according to Embodiment 10 whichillustrates an example of a binarization table indicating a case inwhich the prediction mode values are encoded by binarization. FIG. 113is a diagram according to Embodiment 10 which illustrates an example ofa table indicating predicted values calculated according to therespective prediction modes by the three-dimensional data decodingdevice.

For example, as illustrated in FIG. 108 , FIG. 111 illustrates anexample in which, when the total number of prediction modes denoted by Mis five and the number of three-dimensional points denoted by N is two,as predicted values calculated according to prediction modes, (i) anaverage value is assigned to prediction mode 0, (ii) attribute values ofN three-dimensional points, where N denotes two, are respectivelyassigned to prediction mode 1 and prediction mode 2, and (iii) apredicted value is not assigned to prediction mode 3 and prediction mode4. N three-dimensional points are in the vicinity of a three-dimensionalpoint to be processed and can be used for prediction. In such a case, apredetermined fixed value is assigned as a predicted value calculatedaccording to prediction mode 3 and prediction mode 4 to both of which apredicted value is not assigned.

That is, in calculation of a predicted value, the three-dimensional dataencoding device calculates a predetermined fixed value as a predictedvalue calculated according to a prediction mode, when a predicted valuebased on attribute information of three-dimensional points in thevicinity of a three-dimensional point to be processed is not assigned toa selected prediction mode. For example, in calculation of a predictedvalue, a case in which a predicted value based on attribute informationof three-dimensional points in the vicinity of a three-dimensional pointto be processed is not assigned to a selected prediction mode is a casein which a prediction value is not assigned to the prediction mode dueto the number of three-dimensional points in the vicinity of thethree-dimensional point to be processed being less than or equal to apredetermined number. It should be noted that a case in which apredicted value based on attribute information of three-dimensionalpoints in the vicinity of the three-dimensional point to be processed isnot assigned to the selected prediction mode due to the number ofthree-dimensional points, denoted by N, in the vicinity of thethree-dimensional point to be processed being less than or equal to thepredetermined number is a case in which an average value and attributeinformation of the three-dimensional points in the vicinity of thethree-dimensional point to be processed are assigned as predictedvalues, or in other words, a case in which N+1<M, for example.

For example, as illustrated in FIG. 112 , although the three-dimensionaldata encoding device erroneously selects prediction mode 3 to which apredicted value is not assigned, the three-dimensional data encodingdevice can arithmetically encode “1110” that is binary data of “3” whichis a prediction mode value indicating prediction mode 3. Thearithmetically encoded binary data is appended to the bitstream. Itshould be noted that an example illustrated in FIG. 112 indicates binarydata obtained as a result of binarizing prediction mode values usingtruncated unary coding according to the value of M, where M denotes thetotal number of prediction modes and is five. It should be noted thatthe diagram illustrated in FIG. 112 is the same as the diagramillustrated in FIG. 109 .

Then, as illustrated in FIG. 113 , the three-dimensional data decodingdevice can obtain “1110” that is binary data of “3” which is theprediction mode value indicating prediction mode 3, by decoding dataincluded in the bitstream after obtaining the bitstream generated by thethree-dimensional data encoding device. Since the three-dimensional datadecoding device needs to calculate a predicted value according toprediction mode 3 to which a predicted value is not assigned, thethree-dimensional data decoding device calculates a predetermined fixedvalue as the predicted value calculated according to prediction mode 3.Accordingly, even if a prediction mode to which a predicted value is notassigned is erroneously selected, a predicted value calculated by theencoder and a predicted value calculated by the decoder coincide witheach other. With this, the three-dimensional data decoding device cancorrectly decode the bitstream.

In this way, the three-dimensional data encoding device calculates oneor more predicted values of attribute information of a three-dimensionalpoint to be processed using attribute information of N three-dimensionalpoints in the vicinity of the three-dimensional point to be processed.Then, the three-dimensional data encoding device assigns the one or morepredicted values calculated according to M prediction modes, where Mdenotes the total number of prediction modes, and assigns apredetermined fixed value to a prediction mode to which the one or morepredicted values are not assigned.

It should be noted that the predetermined fixed value is an initialvalue, and is, for example, a value of zero. That is, when an attributevalue indicates color information (such as YUV and RGB), thethree-dimensional data encoding device and the three-dimensional datadecoding device may assign a value of (0, 0, 0) as an initial value, andwhen an attribute value indicates a degree of reflection, thethree-dimensional data encoding device and the three-dimensional datadecoding device may assign a value of zero as an initial value.

It should be noted that, as a method of assigning an initial value, thethree-dimensional data encoding device and the three-dimensional datadecoding device assign an initial value to a prediction mode to which apredicted value is not assigned, but the method of assigning an initialvalue is not limited to this aforementioned method. Examples of themethod will be described with reference to FIG. 114 and FIG. 115 .

FIG. 114 illustrates an example according to Embodiment 10 whichillustrates an initial table used for processing of assigning predictedvalues to the prediction modes. FIG. 115 is a diagram according toEmbodiment 10 which illustrates an example of a table which is generatedusing the initial table and in which predicted values are calculatedaccording to the respective prediction modes by the three-dimensionaldata encoding device.

The three-dimensional data encoding device and the three-dimensionaldata decoding device generate an initial table by assigning, in advance,an initial value (e.g. a value of zero) as a predicted value calculatedaccording to a prediction mode to all prediction modes, for example.Next, the three-dimensional data encoding device and thethree-dimensional data decoding device may update the predicted valuesassigned to the respective prediction modes with an average value ofattribute values of three-dimensional points that can be used forsubsequent prediction and are in the vicinity of a three-dimensionalpoint to be processed, or with attribute values of the three-dimensionalpoints in the vicinity of the three-dimensional point to be processed.

For example, the three-dimensional data encoding device and thethree-dimensional data decoding device may generate, using a table inwhich an initial value is assigned as a predicted value to allprediction modes as in the initial table illustrated in FIG. 114 , atable indicating predicted values calculated according to the respectiveprediction modes by the three-dimensional data encoding device asillustrated in FIG. 115 . Next, the three-dimensional data encodingdevice calculates predicted values according to the respectiveprediction modes, using attribute values of N three-dimensional pointsin the vicinity of the three-dimensional point to be processed. Then,the three-dimensional data encoding device updates the initial values inthe initial table with the predicted values calculated according to therespective prediction modes.

For example, the three-dimensional data encoding device and thethree-dimensional data decoding device may generate a table indicatingpredicted values calculated according to respective prediction modes byupdating the initial value assigned to prediction mode 0 with an averagevalue of attribute values of three-dimensional points that can be usedfor prediction and are in the vicinity of a three-dimensional point tobe processed, and by updating the initial values assigned to predictionmode 1 and prediction mode 2 with attribute values of thethree-dimensional points in the vicinity of the three-dimensional pointto be processed. An initial value is to be assigned to a prediction modeto which a predicted value is not assigned also in this case.

As such, when N+1 is less than M when there are M prediction modes and Nthree-dimensional points in the vicinity of a three-dimensional point tobe encoded, an initial value may be assigned as a predicted valuecalculated according to a prediction mode to which a predicted value isnot assigned, or the initial value may be determined as the predictedvalue due to a predicted value not being assigned, so long as an initialvalue is assigned to a prediction mode to which a predicted valueaccording to attribute values of the N three-dimensional points is notassigned in the end.

It should be noted that so long as the same value is used by the encoderand the decoder, the initial value is not limited to a value of zero,and any values may be used.

FIG. 116 is a flowchart according to Embodiment 10 which illustrates anexample of processing of calculating a predicted value.

First, as illustrated in FIG. 116 , the three-dimensional data encodingdevice calculates a weighted average value of attribute values of Nthree-dimensional points that can be used for prediction of a predictedvalue of a target three-dimensional point to be processed and are in thevicinity of the target three-dimensional point, and assigns thecalculated weighted average value to a prediction mode whose predictionmode value is “0” (S3901).

Next, the three-dimensional data encoding device calculates maximumabsolute difference value maxdiff of the attribute values of the Nthree-dimensional points in the vicinity of the target three-dimensionalpoint to be encoded (S3902).

Next, the three-dimensional data encoding device determines whethermaximum absolute difference value maxdiff is less than threshold Thfix(S3903). It should be noted that threshold Thfix may be encoded andappended to, for example, a header of a stream.

When maximum absolute difference value maxdiff is less than thresholdThfix (YES in S3903), the three-dimensional data encoding device sets“1” to a prediction mode fixation flag and arithmetically encodes theprediction mode fixation flag (S3904), and determines the predictionmode value as “0” (S3905).

On the other hand, when maximum absolute difference value maxdiff isgreater than or equal to threshold Thfix (NO in S3903), thethree-dimensional data encoding device sets “0” to the prediction modefixation flag and arithmetically encodes the prediction mode fixationflag (S3906), and selects one prediction mode from a plurality ofprediction modes (S3907).

Then, the three-dimensional data encoding device arithmetically encodesa prediction mode value indicating the selected prediction mode (S3908).

The three-dimensional data encoding device either outputs the predictionmode value determined in step S3905, or calculates a predicted valueaccording to the prediction mode selected in step S3908 and outputs thecalculated predicted value (S3909). When the prediction mode valuedetermined in step S3905 is used, the three-dimensional data encodingdevice calculates an average of attribute values of N three-dimensionalpoints in the vicinity of a three-dimensional point to be to beprocessed as a predicted value calculated according to a prediction modewhose prediction mode value is indicated as “0”.

It should be noted that the three-dimensional data encoding device mayarithmetically encodes prediction mode values each indicating aprediction mode (PredMode) by binarizing the prediction values usingtruncated unary coding according to M, where M denotes the total numberof prediction modes. In addition, the three-dimensional data encodingdevice may encode NumPredMode indicating the total number of predictionmodes, which is denoted by M, and append NumPredMode to a header. Withthis, the three-dimensional data decoding device can correctly decodethe prediction mode values each indicating a prediction mode (PredMode)by decoding NumPredMode in the header. It should be noted that thethree-dimensional data encoding device need not encode PredMode whenNumPredMode=1. This reduces a code amount since PredMode need not beencoded when NumPredMode=1.

FIG. 117 is a flowchart according to Embodiment 10 which illustratesprocessing of selecting a prediction mode (S3907).

First, the three-dimensional data encoding device assigns, as predictedvalues calculated according to prediction modes, attribute informationof the N three-dimensional points in the vicinity of the targetthree-dimensional point, in the order from attribute information of athree-dimensional point closer to the target three-dimensional point, toprediction modes that are indicated in prediction mode values from “1”to “N” which are incremented by one (S3911). It should be noted thatsince prediction mode values “0” to “N” are assigned, the total numberof prediction modes generated is expressed by N+1. When N+1 exceeds themaximum number of prediction modes (NumPredMode), which is denoted by Mand is appended to a bitstream, the three-dimensional data encodingdevice may generate prediction modes up to M prediction modes.

Next, the three-dimensional data encoding device assigns an initialvalue (e.g. a value of “0”) as a predicted value calculated according toa prediction mode to which a predicted value is not assigned (S3912).Specifically, the three-dimensional data encoding device stores, inmemory, the initial value and the prediction mode to which a predictedvalue is not assigned in association with each other.

Next, the three-dimensional data encoding device calculates cost of eachprediction mode, and selects a prediction mode with minimum cost(S3913).

FIG. 118 is a flowchart according to Embodiment 10 which illustratesprocessing of selecting a prediction mode with minimum cost (S3913).FIG. 118 is a diagram corresponding to the flowchart illustrated in FIG.97 .

First, the three-dimensional data encoding device sets, as initialvalues, zero to i (i=0), and sets infinity to mincost (mincost=∞)(S3921). The initial values set to i and mincost are stored in thememory of the three-dimensional data encoding device.

Next, the three-dimensional data encoding device calculates, using, forexample, formula D1, cost[i] that is the cost of PredMode[i] which isthe i-th prediction mode (S3922).

Next, the three-dimensional data encoding device determines whether thecalculated cost[i] is less than mincost stored in the memory (S3923).

Next, when the calculated cost[i] is less than mincost stored in thememory (YES in S3923), the three-dimensional data encoding devicedetermines cost[i] as mincost (mincost=cost[i]) and PredMode[i] as aprediction mode (S3924), and then proceeds to step S3925. That is, thethree-dimensional data encoding device updates the value of mincoststored in the memory to the value of cost[i], and stores PredMode[i] asthe prediction mode in the memory.

On the other hand, when cost[i] that is the calculated cost is greaterthan or equal to mincost stored in the memory (NO in S3923), thethree-dimensional data encoding device proceeds to step S3925.

Next, the three-dimensional data encoding device increments the value ofi by one (S3925).

Next, the three-dimensional data encoding device determines whether i isless than the number of prediction modes (S3926).

When i is less than the number of the prediction modes (YES in S3926),the three-dimensional data encoding device returns to step S3922, andwhen i is not less than the number of the prediction modes (NO inS3926), the three-dimensional data encoding device terminates theprocessing of selecting a prediction mode with minimum cost.

Hereinafter, processing performed by the three-dimensional data decodingdevice will be described. FIG. 119 is a flowchart according toEmbodiment 10 which illustrates three-dimensional data decodingprocessing performed by the three-dimensional data decoding device.First, the three-dimensional data decoding device decodes geometryinformation (geometry) from a bitstream (S3931). For example, thethree-dimensional data decoding device performs decoding using an octreerepresentation.

Next, the three-dimensional data decoding device decodes attributeinformation from the bitstream (S3932). For example, when thethree-dimensional data decoding device decodes a plurality of types ofattribute information, the three-dimensional data decoding device maydecode the plurality of types of attribute information one by one. Forexample, when the three-dimensional data decoding device decodes a colorand a degree of reflection as attribute information, thethree-dimensional data decoding device decodes an encoded result of thecolor and an encoded result of the degree of reflection in the order inwhich the color and the degree of reflection are appended to thebitstream. For example, when the encoded result of the degree ofreflection is appended to the bitstream after the encoded result of thecolor is appended to the bitstream, the three-dimensional data decodingdevice decodes the encoded result of the color and then decodes theencoded result of the degree of reflection. It should be noted that thethree-dimensional data decoding device may decode encoded results of aplurality of types of attribute information which are appended to thebitstream in any order.

In addition, the three-dimensional data decoding device may obtaininformation indicating a starting location of encoded data of each ofitems of attribute information in the bitstream by decoding, forexample, a header. This allows the three-dimensional data decodingdevice to selectively decode an item of attribute information that needsto be decoded, and thus the process of decoding an item of attributeinformation which does not need to be decoded can be omitted.Accordingly, the amount of processing performed by the three-dimensionaldata decoding device can be reduced. In addition, the three-dimensionaldata decoding device may decode a plurality of types of attributeinformation in parallel, and may integrate decoded results into onethree-dimensional point cloud. With this, the three-dimensional datadecoding device can rapidly decode a plurality of types of attributeinformation.

FIG. 120 is a flowchart according to Embodiment 10 which illustrates theattribute information decoding processing (S3932).

First, the three-dimensional data decoding device determines levels ofdetail (LoDs) (S3941). That is, the three-dimensional data decodingdevice assigns, to any of the LoDs, a plurality of three-dimensionalpoints that are decoded and include geometry information. For example,this assigning method is the same as the assigning method employed bythe three-dimensional data encoding device.

Next, the three-dimensional data decoding device executes a loop per LoDbasis (S3942). That is, the three-dimensional data decoding devicerepeatedly performs, per LoD, processing of steps S3943 through S3949.

Next, the three-dimensional data decoding device executes a loop perthree-dimensional point basis (S3943). That is, the three-dimensionaldata decoding device repeatedly performs, per three-dimensional point,processing of steps S3944 through S3948.

First, the three-dimensional data decoding device searches for aplurality of surrounding points that are three-dimensional points usedfor calculation of a predicted value of a target three-dimensional pointto be processed and are present in the vicinity of the targetthree-dimensional point (S3944). It should be noted that this processingis the same as the processing performed by the three-dimensional dataencoding device.

Here, besides appending NumOfPoint that is the number ofthree-dimensional points per layer to, for example, a header, thethree-dimensional data decoding device separately performs processing ofdecoding prediction modes (PredMode) in a bitstream and processing ofcalculating the plurality of surrounding three-dimensional points whichcan be used for prediction after the LoDs are generated. Specifically,the three-dimensional data decoding device separately performs (i)generation of LoDs (S3941) and searching of surrounding points (S3944),and (ii) calculation of predicted values (S3946) and arithmetic decodingof an n-bit code and a remaining code (decoding of a prediction mode anda prediction residual in S3945). Accordingly, the three-dimensional datadecoding device may perform steps S3941, S3944, and S3946, and stepS3945 in parallel. This can reduce time required for overall processing.

The three-dimensional data decoding device arithmetically decodes aprediction mode and a prediction residual from a bitstream of attributeinformation (S3945).

Next, the three-dimensional data decoding device calculates, accordingto the prediction mode obtained in step S3945, predicted value P of thetarget three-dimensional point (S3946). It should be noted that detailsof processing of calculating the predicted value will be described laterwith reference to FIG. 121 .

In addition, the three-dimensional data decoding device calculates aninverse-quantized value by inverse quantizing a quantized value that isdecoded as the prediction residual (S3947).

Next, the three-dimensional data decoding device generates a decodedvalue by adding the predicted value to the inverse-quantized value(S3948).

Next, the three-dimensional data decoding device terminates the loopexecuted per three-dimensional point (S3949).

The three-dimensional data decoding device also terminates the loopexecuted per LoD (S3950).

FIG. 121 is a flowchart according to Embodiment 10 which illustratesprocessing of calculating a predicted value (S3946).

First, the three-dimensional data decoding device calculates a weightedaverage value of attribute values of N three-dimensional points that canbe used for calculation of a predicted value of a targetthree-dimensional point to be processed and are in the vicinity of thetarget three-dimensional point, and assigns the calculated weightedaverage value to a prediction mode whose prediction mode value is “0”(S3951).

Next, the three-dimensional data decoding device assigns as predictedvalues calculated according to the prediction modes, attributeinformation of the N three-dimensional points in the vicinity of thetarget three-dimensional point, in the order from attribute informationof a three-dimensional point closer to the target three-dimensionalpoint, to prediction modes that are indicated in prediction mode valuesfrom “1” to “N” which are incremented by one (S3952). It should be notedthat since the prediction mode values are assigned from “₀” to “N”, thetotal number of prediction modes generated is expressed by N+1. When thetotal number of prediction modes expressed by N+1 exceeds the number ofthe maximum prediction modes (NumPredMode), which is denoted by M and isappended to the bitstream, the three-dimensional data decoding devicemay generate prediction modes up to M prediction modes.

Next, the three-dimensional data decoding device assigns an initialvalue (e.g. a value of “0”) as a predicted value calculated according toa prediction mode to which a predicted value is not assigned (S3953).Specifically, the three-dimensional data decoding device stores, inmemory, the initial value and the prediction mode to which a predictedvalue is not assigned in association with each other.

Next, the three-dimensional data decoding device outputs a predictedvalue calculated according to a prediction mode that is indicated by theprediction mode value decoded in “decoding of a prediction mode and aprediction residual” in step S3945 (S3954).

It should be noted that, instead of performing step S3953 and stepS3954, the three-dimensional data decoding device may determine whethera value (e.g. a value expressed by “prediction mode value+1”) based on aprediction mode value indicating a prediction mode is greater than thenumber of predicted values calculated based on attribute information ofthree-dimensional points in the vicinity of a target three-dimensionalpoint to be processed, and may assign a predetermined fixed value as apredicted value calculated according to the prediction mode when thevalue based on a prediction mode value is greater than the number ofpredicted values. For example, when a decoded prediction mode (PredMode)indicates a prediction mode to which a prediction value is not assigned(i.e. PredMode>N), the three-dimensional data decoding device may outputan initial value (i.e. a value of “0”) as a predicted value. Inaddition, when the decoded prediction mode (PredMode) indicates aprediction mode to which a predicted value is not assigned (i.e.PredMode>N), the three-dimensional data decoding device may define thecase as violation of standards, and may output an error signalindicating violation of standards.

Although FIG. 116 through FIG. 121 illustrate examples in which each ofthe three-dimensional data encoding device and the three-dimensionaldata decoding device performs processing of assigning an initial valueto a prediction mode to which a predicted value is not assigned, thethree-dimensional data encoding device and the three-dimensional datadecoding device may calculate predicted values according to predictionmodes by updating the predicted values after generating an initial tableas described with reference to FIG. 114 and FIG. 115 .

For example, the three-dimensional data encoding device may perform theprocessing illustrated in FIG. 122 and FIG. 123 instead of performingthe processing illustrated in FIG. 116 and FIG. 117 among the processingdescribed with reference to FIG. 116 through FIG. 118 .

FIG. 122 is a flowchart according to Embodiment 10 which illustratesanother example of processing of calculating a predicted value. Adifference from FIG. 116 is that FIG. 122 includes step S3900 inaddition to the processing illustrated in FIG. 116 . Another differencefrom FIG. 116 is that step S3907 a is performed instead of step S3907.

In this case, as illustrated in FIG. 122 , the three-dimensional dataencoding device assigns, in the first place, an initial value (e.g. avalue of “0”) as a predicted value calculated according to a predictionmode to all prediction modes (S3900). That is, the three-dimensionaldata encoding device generates an initial table.

Next, the three-dimensional data encoding device performs steps S3901through S3906, S3907 a, S3908, and S3909. Here, step S3907 a which is adifference from FIG. 116 will be described with reference to FIG. 123 .

FIG. 123 is a flowchart according to Embodiment 10 which illustratesprocessing of selecting a prediction mode (S3907 a).

The three-dimensional data encoding device performs steps S3911 andS3913 among steps S3911 through S3913 illustrated in FIG. 117 . Byperforming step S3911, a calculated average value or a calculatedattribute value updates an initial value assigned to a prediction modethat corresponds to the calculated average value or the calculatedattribute value.

In addition, for example, the three-dimensional data decoding device mayperform processing illustrated in FIG. 124 instead of performingprocessing described with reference to FIG. 121 among the processingdescribed with reference to FIG. 12 through FIG. 14 .

FIG. 124 is a flowchart according to Embodiment 10 which illustratesprocessing of calculating a predicted value (S3946 a).

The three-dimensional data decoding device assigns an initial value(e.g. a value of “0”) as a predicted value calculated according to aprediction mode to all prediction modes (S3950).

Next, the three-dimensional data decoding device performs steps S3951,S3952, and S3954 which are illustrated in FIG. 121 .

Next, configurations of the three-dimensional data encoding device andthe three-dimensional data decoding device according to the embodimentwill be described. FIG. 125 is a block diagram according to Embodiment10 which illustrates a configuration of three-dimensional data encodingdevice 3900. This three-dimensional data encoding device 3900 includesprediction mode selector 3901, predicted value calculator 3902,prediction residual calculator 3903, and encoder 3904.

Prediction mode selector 3901 selects one prediction mode among two ormore prediction modes for calculating a predicted value of attributioninformation of a first three-dimensional point, using attributioninformation of one or more second three-dimensional points in thevicinity of the first three-dimensional point. Predicted valuecalculator 3902 calculates a predicted value according to the selectedprediction mode. Predicted value calculator 3902 calculates, incalculation of the predicted value, a predetermined fixed value as thepredicted value calculated according to the selected prediction mode ina case in which a predicted value based on attribute information of thesecond three-dimensional points is not assigned to the selectedprediction mode due to the number of the second three-dimensional pointsbeing less than or equal to a predetermined number. Prediction residualcalculator 3903 calculates a prediction residual that indicates adifference between the attribute information of the firstthree-dimensional point and the calculated predicted value. Encoder 3904generates a bitstream that includes the prediction mode and theprediction residual.

FIG. 126 is a block diagram according to Embodiment 10 which illustratesa configuration of three-dimensional data decoding device 3910. Thisthree-dimensional data decoding device 3910 includes obtainer 3911,predicted value calculator 3912, and decoder 3913. Obtainer 3911 obtainsthe prediction mode and the prediction residual of a firstthree-dimensional point among a plurality of three-dimensional points byobtaining a bitstream. Predicted value calculator 3912 calculates apredicted value according to the obtained prediction mode. Predictedvalue calculator 3912 calculates, in calculation of the predicted value,a predetermined fixed value as a predicted value calculated according tothe obtained prediction mode in a case in which the predicted valuebased on attribute information of one or more second three-dimensionalpoints in the vicinity of the first three-dimensional point is notassigned to the obtained prediction mode due to the number of the one ormore second three-dimensional points in the vicinity of the firstthree-dimensional point being less than or equal to a predeterminednumber. Decoder 3913 calculates the attribute information of the firstthree-dimensional point by adding the predicted value and the predictionresidual.

Three-dimensional data encoding device according to the embodimentperforms processing illustrated in FIG. 127 .

The three-dimensional encoding device selects one prediction mode amongtwo or more prediction modes for calculating a predicted value ofattribution information of a first three-dimensional point, usingattribution information of one or more second three-dimensional pointsin the vicinity of the first three-dimensional point (S3961). Next, thethree-dimensional data encoding device calculates a predicted valueaccording to the selected prediction mode (S3962). In calculation of thepredicted value (S3962), the three-dimensional data encoding devicecalculates a predetermined fixed value as a predicted value calculatedaccording to the prediction mode in a case in which a predicted valuebased on attribute information of the second three-dimensional points inthe vicinity of the first three-dimensional point is not assigned to theselected prediction mode due to the number of the secondthree-dimensional points being less than or equal to a predeterminednumber. Next, the three-dimensional data encoding device generates abitstream that includes the prediction mode and a prediction residual(S3964).

According to the processing described above, a predetermined fixed valueis assigned as a predicted value to a prediction mode to which one ormore predicted values are not assigned. For this reason, a bitstream tobe generated includes a prediction mode and a prediction residual thatis calculated according to a predicted value calculated according to aselected prediction mode. Accordingly, when decoding attributeinformation of a three-dimensional point to be processed from anobtained bitstream, the three-dimensional data decoding device cancalculate a predicted value that coincides with a predicted value thatis calculated in the encoding, since, as a predicted value, apredetermined fixed value is assigned to the prediction mode to whichone or more predicted values are not assigned in the same manner as thethree-dimensional data encoding method. Therefore, the three-dimensionaldata decoding device can correctly decode the attribute information ofthe three-dimensional point to be processed.

In addition, the predetermined fixed value is an initial value.Moreover, the predetermined fixed value is zero.

In addition, in calculation of one or more predicted values (S3962), anaverage of attribute information of the one or more secondthree-dimensional points is calculated as a predicted value calculatedaccording to a first prediction mode among two or more prediction modes,and attribute information of the one or more second three-dimensionalpoints is calculated as a predicted value calculated according to asecond prediction mode among the two or more prediction modes.

In addition, the two or more prediction modes are indicated byprediction mode values each having a different value. A prediction modevalue that indicates a prediction mode to which the average is assignedas a predicted value is less than a prediction mode value that indicatesa prediction mode to which the attribute information of the one or moresecond three-dimensional points is assigned as a predicted value.

In addition, the two or more prediction modes are indicated byprediction mode values each having a different value. A prediction modevalue that indicates a prediction mode to which attribute information ofone second three-dimensional point is assigned as a predicted value isless than a prediction mode value that indicates a prediction mode towhich attribute information of another second three-dimensional point,which is located further away from the first third-three dimensionalpoint than the one second three-dimensional point, is assigned as apredicted value.

The three-dimensional data decoding device according to the embodimentperforms processing illustrated in FIG. 128 .

The three-dimensional data decoding device obtains a prediction mode anda prediction residual of a first three-dimensional point among aplurality of three-dimensional points by obtaining a bitstream (S3971).Next, the three-dimensional data decoding device calculates a predictedvalue according to the obtained prediction mode (S3972). In calculationof the predicted value (S3972), the three-dimensional data decodingdevice calculates a predetermined fixed value as a predicted valuecalculated according to the prediction mode in a case in which apredicted value based on attribute information of one or more secondthree-dimensional points in the vicinity of the first three-dimensionalpoint is not assigned to the obtained prediction mode due to the numberof the one or more second three-dimensional points being less than orequal to a predetermined number. Next, the three-dimensional datadecoding device calculates attribute information of the firstthree-dimensional point by adding the predicted value and the predictionresidual (S3973).

According to the processing described above, when decoding attributeinformation of a three-dimensional point to be processed from theobtained bitstream, a predicted value that coincides with a predictedvalue that is calculated in the encoding can be calculated, since, as apredicted value, a predetermined fixed value is assigned to a predictionmode to which one or more predicted values are not assigned in the samemanner as the three-dimensional data encoding method. Therefore, theattribute information of the three-dimensional point to be processed canbe correctly decoded.

In addition, the predetermined fixed value is an initial value.Moreover, the predetermined fixed value is zero.

In addition, in the calculation of one or more predicted values (S3972),an average of attribute information of the one or more secondthree-dimensional points is calculated as a predicted value calculatedaccording to a first prediction mode among two or more prediction modes,and attribute information of the one or more second three-dimensionalpoints is calculated as a predicted value calculated according to asecond prediction mode among the two or more prediction modes.

In addition, the two or more prediction modes are indicated byprediction mode values each having a different value. A prediction modevalue that indicates a prediction mode to which the average is assignedas a predicted value is less than a prediction mode value that indicatesa prediction mode to which the attribute information of the one or moresecond three-dimensional points is assigned as a predicted value.

In addition, the two or more prediction modes are indicated byprediction mode values each having a different value. A prediction modevalue that indicates a prediction mode to which attribute information ofone second three-dimensional point is assigned as a predicted value isless than a prediction mode value that indicates a prediction mode towhich attribute information of another second three-dimensional point,which is located further away from the first third-three dimensionalpoint than the one second three-dimensional point, is assigned as apredicted value.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to the embodiments of thepresent disclosure have been described above, but the present disclosureis not limited to these embodiments.

Note that each of the processors included in the three-dimensional dataencoding device, the three-dimensional data decoding device, and thelike according to the above embodiments is typically implemented as alarge-scale integrated (LSI) circuit, which is an integrated circuit(IC). These may take the form of individual chips, or may be partiallyor entirely packaged into a single chip.

Such IC is not limited to an LSI, and thus may be implemented as adedicated circuit or a general-purpose processor. Alternatively, a fieldprogrammable gate array (FPGA) that allows for programming after themanufacture of an LSI, or a reconfigurable processor that allows forreconfiguration of the connection and the setting of circuit cellsinside an LSI may be employed.

Moreover, in the above embodiments, the structural components may beimplemented as dedicated hardware or may be implemented by executing asoftware program suited to such structural components. Alternatively,the structural components may be implemented by a program executor suchas a CPU or a processor reading out and executing the software programrecorded in a recording medium such as a hard disk or a semiconductormemory.

The present disclosure may also be implemented as a three-dimensionaldata encoding method, a three-dimensional data decoding method, or thelike executed by the three-dimensional data encoding device, thethree-dimensional data decoding device, and the like.

Also, the divisions of the functional blocks shown in the block diagramsare mere examples, and thus a plurality of functional blocks may beimplemented as a single functional block, or a single functional blockmay be divided into a plurality of functional blocks, or one or morefunctions may be moved to another functional block. Also, the functionsof a plurality of functional blocks having similar functions may beprocessed by single hardware or software in parallelized or time-dividedmanner.

Also, the processing order of executing the steps shown in theflowcharts is a mere illustration for specifically describing thepresent disclosure, and thus may be an order other than the above order.Also, one or more of the steps may be executed simultaneously (inparallel) with another step.

A three-dimensional data encoding device, a three-dimensional datadecoding device, and the like according to one or more aspects have beendescribed above based on the embodiments, but the present disclosure isnot limited to these embodiments. The one or more aspects may thusinclude forms achieved by making various modifications to the aboveembodiments that can be conceived by those skilled in the art, as wellforms achieved by combining structural components in differentembodiments, without materially departing from the spirit of the presentdisclosure.

Although only some exemplary embodiments of the present disclosure havebeen described in detail above, those skilled in the art will readilyappreciate that many modifications are possible in the exemplaryembodiments without materially departing from the novel teachings andadvantages of the present disclosure. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure.

INDUSTRIAL APPLICABILITY

The present disclosure is applicable to a three-dimensional dataencoding device and a three-dimensional data decoding device.

What is claimed is: 1-16. (canceled)
 17. A three-dimensional dataencoding method, the three-dimensional data encoding method comprising:(i) in a case in which a number of reference values available tocalculate a predicted value of a current value in one prediction modeselected from among two or more prediction modes is greater than apredetermined number corresponding to the one prediction mode selected,(a) calculating the predicted value according to the one prediction modeselected and (b) calculating a prediction residual that is a differencebetween the current value and the predicted value calculated; (ii) in acase in which the number is equal to or less than the predeterminednumber, not performing the calculation of the predicted value; andgenerating a bitstream that includes the prediction residual.
 18. Thethree-dimensional data encoding method according to claim 17, wherein(ii) in the case in which the number is equal to or less than thepredetermined number, the current value is assigned as the predictionresidual.
 19. A three-dimensional data decoding method, thethree-dimensional data decoding method comprising: (i) in a case inwhich a number of reference values available to calculate a predictedvalue of a current value in one prediction mode selected from among twoor more prediction modes is greater than a predetermined numbercorresponding to the one prediction mode selected, (a) obtaining abitstream that includes one prediction mode and a prediction residual,(b) calculating the predicted value according to the one prediction modeobtained, and (c) calculating the current value by adding the predictedvalue and the prediction residual; and (ii) in a case in which the totalnumber is equal to or less than the predetermined number, not performingthe calculation of the predicted value.
 20. The three-dimensional datadecoding method according to claim 19, wherein (ii) in the case in whichthe total number is equal to or less than the predetermined number, theprediction residual is assigned as the current value.